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Figure 1.
The eFACE Scale
The eFACE Scale

Graphical user interface eFACE input screen, demonstrating 15 variables grouped into 3 categories: static (4 variables), dynamic (7), and synkinesis (4).

Figure 2.
Score Distributions
Score Distributions

Histograms demonstrating the distribution of expert-rated disfigurement severity scores (A) and eFACE static (B), eFACE dynamic (C), and eFACE synkinesis (D) subset scores. For disfigurement scoring, “0” represents the most extreme disfigurement possible from a facial nerve disorder, and “100” represents no discernible disfigurement. For eFACE scoring, “0” represents the most extreme facial asymmetry possible, and “100” represent complete symmetry with the contralateral.

Figure 3.
eFACE Model Predicted vs Expert-Graded Disfigurement Scores
eFACE Model Predicted vs Expert-Graded Disfigurement Scores

A plot of predicted disfigurement scores based on the developed mathematical model for which inputs are expert-rated eFACE subset scores vs actual expert-rated disfigurement scores demonstrates high correlation (r2 = 0.79; P < .05 for all 3 variables). The equation governing the model is as follows: predicted disfigurement = −38.895 + [0.488 (static eFACE score)] + [0.761 (dynamic eFACE score)] + [0.119 (synkinesis eFACE score)]. For disfigurement scoring, “0” represents the most extreme disfigurement possible from a facial nerve disorder, and “100” represents no discernible disfigurement.

Figure 4.
Weighted Scores of Individual eFACE Variables to Disfigurement
Weighted Scores of Individual eFACE Variables to Disfigurement

A hybrid regression model was used to determine the relative contribution of each eFACE variable to expert-graded disfigurement. NLF indicates nasolabial fold.

Table.  
Interrater Reliability Between Clinician Ratings for Disfigurement and eFACE Scores
Interrater Reliability Between Clinician Ratings for Disfigurement and eFACE Scores
1.
Fattah  AY, Gavilan  J, Hadlock  TA,  et al.  Survey of methods of facial palsy documentation in use by members of the Sir Charles Bell Society.  Laryngoscope. 2014;124(10):2247-2251.PubMedGoogle ScholarCrossref
2.
Fattah  AY, Gurusinghe  AD, Gavilan  J,  et al; Sir Charles Bell Society.  Facial nerve grading instruments: systematic review of the literature and suggestion for uniformity.  Plast Reconstr Surg. 2015;135(2):569-579.PubMedGoogle ScholarCrossref
3.
Jowett  N, Hadlock  TA.  An evidence-based approach to facial reanimation.  Facial Plast Surg Clin North Am. 2015;23(3):313-334.PubMedGoogle ScholarCrossref
4.
House  JW.  Facial nerve grading systems.  Laryngoscope. 1983;93(8):1056-1069.PubMedGoogle ScholarCrossref
5.
Ross  BG, Fradet  G, Nedzelski  JM.  Development of a sensitive clinical facial grading system.  Otolaryngol Head Neck Surg. 1996;114(3):380-386.PubMedGoogle ScholarCrossref
6.
Vrabec  JT, Backous  DD, Djalilian  HR,  et al; Facial Nerve Disorders Committee.  Facial nerve grading system 2.0.  Otolaryngol Head Neck Surg. 2009;140(4):445-450.PubMedGoogle ScholarCrossref
7.
Yanagihara  N. Grading of facial palsy. In: Fisch  U, ed.  Facial Nerve Surgery. Birmingham, AL: Aesculapius Publishing Co; 1977:533-535.
8.
Banks  CA, Bhama  PK, Park  J, Hadlock  CR, Hadlock  TA.  Clinician-graded electronic facial paralysis assessment: the eFACE.  Plast Reconstr Surg. 2015;136(2):223e-230e.PubMedGoogle ScholarCrossref
9.
Bhama  P, Gliklich  RE, Weinberg  JS, Hadlock  TA, Lindsay  RW.  Optimizing total facial nerve patient management for effective clinical outcomes research.  JAMA Facial Plast Surg. 2014;16(1):9-14.PubMedGoogle ScholarCrossref
10.
Iacolucci  CM, Banks  C, Jowett  N,  et al.  Development and validation of a spontaneous smile assay.  JAMA Facial Plast Surg. 2015;17(3):191-196.PubMedGoogle ScholarCrossref
11.
Kahn  JB, Gliklich  RE, Boyev  KP, Stewart  MG, Metson  RB, McKenna  MJ.  Validation of a patient-graded instrument for facial nerve paralysis: the FaCE scale.  Laryngoscope. 2001;111(3):387-398.PubMedGoogle ScholarCrossref
12.
Chu  EA, Farrag  TY, Ishii  LE, Byrne  PJ.  Threshold of visual perception of facial asymmetry in a facial paralysis model.  Arch Facial Plast Surg. 2011;13(1):14-19.PubMedGoogle ScholarCrossref
13.
Dey  JK, Ishii  LE, Byrne  PJ, Boahene  KD, Ishii  M.  Seeing is believing: objectively evaluating the impact of facial reanimation surgery on social perception.  Laryngoscope. 2014;124(11):2489-2497.PubMedGoogle ScholarCrossref
14.
Dey  JK, Ishii  M, Boahene  KD, Byrne  PJ, Ishii  LE.  Changing perception: facial reanimation surgery improves attractiveness and decreases negative facial perception.  Laryngoscope. 2014;124(1):84-90.PubMedGoogle ScholarCrossref
15.
Ishii  L, Godoy  A, Encarnacion  CO, Byrne  PJ, Boahene  KD, Ishii  M.  Not just another face in the crowd: society’s perceptions of facial paralysis.  Laryngoscope. 2012;122(3):533-538.PubMedGoogle ScholarCrossref
16.
Bhama  PK, Weinberg  JS, Lindsay  RW, Hohman  MH, Cheney  ML, Hadlock  TA.  Objective outcomes analysis following microvascular gracilis transfer for facial reanimation: a review of 10 years’ experience.  JAMA Facial Plast Surg. 2014;16(2):85-92.PubMedGoogle ScholarCrossref
17.
Hohman  MH, Hadlock  TA.  Etiology, diagnosis, and management of facial palsy: 2000 patients at a facial nerve center.  Laryngoscope. 2014;124(7):E283-E293.PubMedGoogle ScholarCrossref
18.
Hohman  MH, Kim  SW, Heller  ES, Frigerio  A, Heaton  JT, Hadlock  TA.  Determining the threshold for asymmetry detection in facial expressions.  Laryngoscope. 2014;124(4):860-865.PubMedGoogle ScholarCrossref
19.
Ahmidi  N, Hager  GD, Ishii  L, Fichtinger  G, Gallia  GL, Ishii  M.  Surgical task and skill classification from eye tracking and tool motion in minimally invasive surgery.  Med Image Comput Comput Assist Interv. 2010;13(pt 3):295-302.PubMedGoogle Scholar
20.
Godoy  A, Ishii  M, Byrne  PJ, Boahene  KD, Encarnacion  CO, Ishii  LE.  How facial lesions impact attractiveness and perception: differential effects of size and location.  Laryngoscope. 2011;121(12):2542-2547.PubMedGoogle ScholarCrossref
21.
Ishii  L, Carey  J, Byrne  P, Zee  DS, Ishii  M.  Measuring attentional bias to peripheral facial deformities.  Laryngoscope. 2009;119(3):459-465.PubMedGoogle ScholarCrossref
Original Investigation
Jul/Aug 2016

Weighting of Facial Grading Variables to Disfigurement in Facial Palsy

Author Affiliations
  • 1Division of Facial Plastic and Reconstructive Surgery, Department of Otolaryngology, Harvard Medical School/Massachusetts Eye and Ear Infirmary, Boston
  • 2Department of Mathematical Sciences, Bentley University, Waltham, Massachusetts
 

Copyright 2016 American Medical Association. All Rights Reserved.

JAMA Facial Plast Surg. 2016;18(4):292-299. doi:10.1001/jamafacial.2016.0226
Key Points

Question  Are eFACE facial grading scale scores predictive of overall disfigurement among patients with facial palsy?

Findings  This observational study found that predicted disfigurement scores from eFACE subset scores demonstrated excellent agreement with surgeon-graded disfigurement severity. Variable weighting demonstrated that key contributors to overall disfigurement included nasolabial fold depth and oral commissure position at rest.

Meaning  Key contributors to overall disfigurement in facial palsy include facial asymmetries that may be readily addressed with static reanimation procedures.

Abstract

Importance  A universal, health care professional–graded scale for facial assessment would be a useful tool for reporting, comparing, and assessing facial function among patients with facial paralysis.

Objectives  To correlate scores of an assessment tool, the eFACE scale, with expert-rated facial disfigurement and to determine the relative contributions of facial features to facial palsy–related disfigurement.

Design, Setting, and Participants  The eFACE scale yields 15 individual variable scores, in addition to subscores for static, dynamic, and synkinesis elements, and a total score that is based on 100-point scales. Two hundred patients with varying degrees of unilateral facial palsy underwent independent eFACE assessment and assignment of a disfigurement score by 2 facial nerve surgeons. The mean scores were determined, and multivariate regression analysis was performed to fit eFACE subset scores (static, dynamic, and synkinesis) to disfigurement ratings. A hybrid regression model was then used to weight each of the 15 eFACE variables, using stepwise regression to control for the effect of the other variables. Scoring was performed during an 8-week period from March 16 to May 8, 2015.

Main Outcome and Measure  Use of the 100-point eFACE variables, together with a 100-point visual analog scale of disfigurement, with 0 representing the most extreme disfigurement possible from a facial nerve disorder and 100 representing no discernible facial disfigurement.

Results  In the 200 patients included in analysis (126 [63.0% female]; mean [SD] age, 46.5 [16.4] years]), predicted disfigurement scores based on eFACE subset scores demonstrated excellent agreement with surgeon-graded disfigurement severity (r2 = 0.79). Variable weighting demonstrated that the 6 key contributors to overall disfigurement were (in order of importance) nasolabial fold depth at rest (normalized coefficient [NC], 0.18; P < .001), oral commissure position at rest (NC, 0.15; P < .001), lower lip asymmetry while pronouncing the long /ē/ (NC, 0.09; P < .001), palpebral fissure width at rest (NC, 0.09; P < .001), nasolabial fold orientation with smiling (NC, 0.08; P = .001), and palpebral fissure width during attempts at full eye closure (NC, 0.06; P = .03).

Conclusions and Relevance  A mathematical association between eFACE-measured facial features and overall expert-graded disfigurement in facial paralysis has been established. For those using the eFACE grading scale, predictions of the specific effects of various interventions on expert-rated disfigurement are now possible and may guide therapy.

Level of Evidence  NA.

Introduction

The facial nerve community has long recognized how difficult exchange of information between and among practitioners continues to be, despite major advances in medical and surgical facial reanimation.1-3 The difficulty with communication and results reporting stems largely from lack of a common language or scale of facial function agreed on by the community of facial nerve physicians. There has been a strong tendency toward individual development of personal scales, often named for their developers, rather than acceptance and popularization of a single, universally applicable scale that provides detailed data to health care professionals treating each aspect of facial function.4-7 Recently, we developed a straightforward health care professional–graded facial function scale, the eFACE (Figure 1), which incorporates many of the favorable features of the most widely used scales.8 The scale is rapid to perform, like the House-Brackmann scale,4 provides details regarding static, dynamic, and sykinesis performance, as is the Sunnybrook scale,5 and provides instant, visual graphic output, subscore, and total score computations to easily gauge progress and share with patients during the clinical encounter. In addition, the scale is fully digital on an easy-to-interpret 100-point scale and is available for use on all electronic platforms, including smartphones, tablets, laptops, and desktop devices.8

Significant value of any facial function scale would lie in establishing the association between its scores and overall facial disfigurement. This association, if consistent, would provide predictions regarding the effects of specific interventions or recovery on overall disfigurement and could help guide therapy. The goal of the present study was to mathematically examine the association between individual eFACE scores, subscores, and overall scores to expert-determined facial disfigurement as a step toward understanding the relative importance of each of the measured features to overall disfigurement. Understanding these correlations may assist the facial nerve community in developing clinical treatment priorities and is likely to facilitate comparisons of the effects of various treatments on facial function and appearance.

Methods

Two hundred consecutive patients with unilateral facial palsy presenting to our tertiary care facial nerve center underwent facial function assessment using the eFACE scoring system by 2 independent facial nerve surgeons (C.A.B. and T.A.H.). The scoring was part of routine facial nerve center intake9 and was accompanied by a full history and head and neck examination, as well as standardized facial nerve photography and videography, spontaneous smile assay,10 and the administration of a validated patient-reported outcomes scale—the Facial Clinimetric Evaluation instrument.11 The eFACE scores for each of the 15 characteristics were averaged between both independent experts for all patients to produce a mean eFACE score for each characteristic. In addition, because the raw input data from eFACE scoring includes information concerning the direction of malposition or inappropriate movement for 6 variables that can move in either direction around the normal value, 6 more binary indicator variables were included to capture this further information. The institutional internal review board at the Massachusetts Eye and Ear Infirmary approved this prospective observational study. Patients provided informed written consent; there was no financial compensation.

The experts (C.A.B. and T.A.H.) independently assigned a disfigurement score to each patient, with 0 representing the most extreme disfigurement possible from a facial nerve disorder and 100 representing no discernible disfigurement. The 100-point observer-perceived disfigurement rating was drawn from the reports of investigators who have long studied the association between facial irregularities and disfigurement.12-15 The 2 disfigurement scores were averaged to produce a single disfigurement score for each patient. For each patient, the mean eFACE scores and disfigurement scores were transferred to a database for analysis.

Statistical Analysis

Multivariate regression analysis was performed to fit the eFACE subscores (static, dynamic, and synkinesis) to the disfigurement ratings. To estimate the relative contributions of the 15 fundamental variables to overall disfigurement, it was imperative to avoid the significant multicollinearity issues associated with the coefficients produced by a straightforward multiple regression based on the entire set of variables. However, using simple linear regression 1 variable at a time would not adequately control for the other variables. Furthermore, we wanted to formulate our analysis around the basic physical variables so that the results could best be interpreted and applied; this desire precluded using related techniques, such as factor analysis. Thus, we followed a hybrid regression approach, taking 1 variable at a time as the focus, entering it into a regression model, and then augmenting it by a stepwise regression process to optimally add additional variables (or subsequently remove them) according to typical stepwise regression criteria (P < .05 for entering, P > .10 for removing). This strategy provided appropriate control for the balance of the variables. We used the resulting coefficients from each such regression as a reasonable estimate of contribution. The values of the coefficients were then standardized such that the variances of the dependent and predictor variables were equal to 1 (ie, normalized coefficients were calculated). Because the final step in the stepwise regression process does not always yield the preferred estimate for the particular coefficient of interest, based on the corresponding P values, some judgment was occasionally necessary to select the best compromise, with consideration of r2, P, and variance inflation factor values for each step in each regression sequence. Interrater reliability between expert ratings of disfigurement and for all eFACE variables was assessed using the intraclass correlation coefficient (2-way random, single measures) in IBM SPSS, version 20 (IBM Inc).

Results

Acquisition of eFACE data and disfigurement scoring for the 200 patients occurred during an 8-week period (March 16 to May 8, 2015). The sample included 126 women (63.0%); mean (SD) age was 46.5 (16.4) years. There was a fairly even distribution of severity of expert-rated disfigurement among the patients assessed (Figure 2) and high interrater reliability between the 2 raters for both overall disfigurement and all eFACE variables (Table).

Multivariable linear regression analysis using the 3 subscores of static, dynamic, and synkinesis categories yielded the following equation: predicted disfigurement = −38.895 + [0.488 (static eFACE score)] + [0.761 (dynamic eFACE score)] + [0.119 (synkinesis eFACE score)]. This model showed excellent agreement with the physician rating of disfigurement severity (r2 = 0.79). Figure 3 illustrates the distribution of our sample population in terms of their predicted disfigurement using the expert-rated eFACE subset scores and the expert-rated disfigurement. The model fit the assumptions of linear regression, all 3 coefficients were significant at the 5% level, and the variance inflation factors were low. When we used all 21 variables in our regression, the r2 value increased modestly to 0.84 (with an adjusted r2 value of 0.82), but this did not facilitate the prioritization of clinical factors because of the concomitant multicollinearity issues.

Using a hybrid regression model, we calculated the relative contributions of each variable of the eFACE to disfigurement (Figure 4). The 6 most key contributors to overall disfigurement included, in descending order, nasolabial fold depth at rest (normalized coefficient [NC], 0.177 [normalized standard error (NSE), 0.027]; P < .001), oral commissure position at rest (NC, 0.152 [NSE, 0.024]; P < .001), lower lip asymmetry while pronouncing the long /ē/ (NC, 0.094 [NSE, 0.019]; P < .001), palpebral fissure width at rest (NC, 0.086 [NSE, 0.020]; P < .001), nasolabial fold orientation with smiling (NC, 0.075 [NSE, 0.023]; P < .001), and palpebral fissure width during attempts at full eye closure (NC, 0.063 [NSE, 0.028]; P = .03). Interrater reliability exceeded 0.93 for disfigurement scores and exceeded 0.80 for nearly all eFACE variables (Table).

Discussion

Facial function is difficult to quantify, and measures of recovery or improvement are elusive. Development of the eFACE scale arose through insights gained from evaluation and serial examinations of nearly 3000 patients with facial movement disorders over a 15-year period and was designed to replace the myriad scales used disparately throughout the world and across specialties.16,17 Unlike the House-Brackmann scale,4 which was developed specifically to describe recovery following extirpation of vestibular schwannomas (although erroneously applied to many other populations), the eFACE tool is meant to apply to patients with facial nerve weakness from any source. Scoring of the 15 static, dynamic, and synkinesis eFACE variables using visual analog scales represents a step toward standardization of facial function reporting. The scale provides useful information in scenarios when either flaccidity or hypertonicity and synkinesis predominate and for patients with mixed pictures in which different zones demonstrate elements of each of those states. The scale may also be applied to isolated zonal weaknesses and, when applied serially, can gauge the rate and completeness of recovery or response to intervention. The eFACE facial grading system has been shown to have high interrater reliability, with the pilot study8 demonstrating an intraclass correlation coefficient of 0.97 for the composite score among 3 independent surgeons. Using the present larger data set, the intraclass correlation coefficient was again evaluated and a similar value of 0.94 was obtained for the composite score, as well as comparably significant intraclass correlation coefficient values for each input variable.

The goal of the present investigation was to add to the usefulness of the scale by providing data regarding its association with expert-determined facial disfigurement so that the relative importance of each of the facial features measured could be better understood. The present study applied multivariate regression analysis to eFACE subscore data and permitted prediction of overall expert-rated disfigurement based on eFACE scores. We found that applying multivariate regression to the aggregate subscores of static, dynamic, and synkinesis domains yielded a formula highly predictive of expert-rated disfigurement. The equation reveals that the contributions of dynamic movement asymmetry to overall disfigurement are greatest, followed by static landmark asymmetry. Synkinetic movements carried the smallest importance of the 3 subscores to overall disfigurement; however, a limitation of this study is that, of the 200 patients in our sample, only 1 in 5 demonstrated moderate or severe synkinesis (Figure 2). It is possible that a different sample of patients having worse overall synkinesis may yield a higher effect of that subscore on disfigurement. Another challenge of independently weighting dynamic and synkinesis scores is their collinearity; for example, patients with severe midfacial synkinesis nearly always demonstrate severe limitation in dynamic oral commissure excursion.

The mathematical analysis also provides insight regarding the relative contributions of each of the eFACE-measured features to overall disfigurement and may be used as 1 factor in clinical decision making when developing plans for treatment in patients with facial movement disorders. For individual variable analysis, we found that features involving the midface and mouth were the most important in predicting disfigurement, with the top 3 most relevant variables in this zone (Figure 4). Nasolabial fold depth at rest was the single most important determinant of expert-judged disfigurement, likely because the nasolabial fold occupies a dominant position in the midface; asymmetries are readily apparent to the observer. Likewise, resting oral commissure malposition correlates highly with disfigurement, possibly because of the conspicuous nature of oral commissure asymmetry. The third most important variable contributing to disfigurement using our mathematical modeling approach was dynamic lower lip asymmetry; in many individuals, differences in the amount of dental show based on inferior displacement of the lower lip during smiling can be both noticeable and unnatural, perhaps accounting for its relevance in the disfigurement equation. Observers have been shown to18 be more sensitive to palpebral fissure width asymmetry than asymmetry in other facial zones, so the fact that resting palpebral fissure asymmetry plays another principal role in overall expert-judged disfigurement is not surprising.

Taken together, the data presented here suggest that addressing nasolabial fold asymmetry, oral commissure resting malposition, lower lip asymmetry, and palpebral fissure width asymmetry could contribute significantly to reducing facial disfigurement. The data also suggest that smile restoration will impact reduction of disfigurement in important ways, lending credence to modern approaches to facial reanimation that emphasize dynamic smile reconstruction and the correction of resting oral commissure position.

This study is limited in that it looked only at expert-graded disfigurement severity within a single center. Although the reaction of a naive observer to an individual with a facial movement disorder is the ultimate test of facial disfigurement, layperson assessments of the disfiguring effects of facial paralysis are only beginning to be understood.12-15,19-21 Furthermore, such layperson assessments may vary between cultures. Layperson assessments are very relevant to study against eFACE scores and merit thorough investigation under a similar experimental paradigm as that described herein. However, in the present investigation, we chose to develop a mathematical model using disfigurement scores generated by surgeons with a deep understanding of the entire spectrum of facial paralysis under the assumption that accuracy of the disfigurement ratings with respect to best and worst states would be more precise among individuals who had encountered these extremes.

Conclusions

The predicted disfigurement scores one may calculate using the equation presented here are intended for use by health care professionals both to guide therapy and gauge the effectiveness of certain interventions or spontaneous recovery to the overall clinical picture. Future studies should seek to determine whether the results present herein are valid across other facial nerve centers and for layperson assessments of facial disfigurement.

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Article Information

Corresponding Author: Nate Jowett, MD, Division of Facial Plastic and Reconstructive Surgery, Department of Otolaryngology, Harvard Medical School/Massachusetts Eye and Ear Infirmary, 243 Charles St, Boston, MA 02114 (nathan_jowett@meei.harvard.edu).

Accepted for Publication: March 1, 2016.

Correction: This article was corrected for errors on February 26, 2017. It will also receive a published Correction Notice.

Published Online: April 28, 2016. doi:10.1001/jamafacial.2016.0226

Author Contributions: Dr Banks had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Banks, C. R. Hadlock, T. A. Hadlock.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Banks, C. R. Hadlock, T. A. Hadlock.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: All authors.

Obtained funding: T. A. Hadlock.

Administrative, technical, or material support: T. A. Hadlock.

Study supervision: T. A. Hadlock.

Conflict of Interest Disclosures: None reported.

Previous Presentation: This study was presented at the 2016 annual meeting of the American Society for Peripheral Nerve; January 16, 2016; Scottsdale, Arizona.

References
1.
Fattah  AY, Gavilan  J, Hadlock  TA,  et al.  Survey of methods of facial palsy documentation in use by members of the Sir Charles Bell Society.  Laryngoscope. 2014;124(10):2247-2251.PubMedGoogle ScholarCrossref
2.
Fattah  AY, Gurusinghe  AD, Gavilan  J,  et al; Sir Charles Bell Society.  Facial nerve grading instruments: systematic review of the literature and suggestion for uniformity.  Plast Reconstr Surg. 2015;135(2):569-579.PubMedGoogle ScholarCrossref
3.
Jowett  N, Hadlock  TA.  An evidence-based approach to facial reanimation.  Facial Plast Surg Clin North Am. 2015;23(3):313-334.PubMedGoogle ScholarCrossref
4.
House  JW.  Facial nerve grading systems.  Laryngoscope. 1983;93(8):1056-1069.PubMedGoogle ScholarCrossref
5.
Ross  BG, Fradet  G, Nedzelski  JM.  Development of a sensitive clinical facial grading system.  Otolaryngol Head Neck Surg. 1996;114(3):380-386.PubMedGoogle ScholarCrossref
6.
Vrabec  JT, Backous  DD, Djalilian  HR,  et al; Facial Nerve Disorders Committee.  Facial nerve grading system 2.0.  Otolaryngol Head Neck Surg. 2009;140(4):445-450.PubMedGoogle ScholarCrossref
7.
Yanagihara  N. Grading of facial palsy. In: Fisch  U, ed.  Facial Nerve Surgery. Birmingham, AL: Aesculapius Publishing Co; 1977:533-535.
8.
Banks  CA, Bhama  PK, Park  J, Hadlock  CR, Hadlock  TA.  Clinician-graded electronic facial paralysis assessment: the eFACE.  Plast Reconstr Surg. 2015;136(2):223e-230e.PubMedGoogle ScholarCrossref
9.
Bhama  P, Gliklich  RE, Weinberg  JS, Hadlock  TA, Lindsay  RW.  Optimizing total facial nerve patient management for effective clinical outcomes research.  JAMA Facial Plast Surg. 2014;16(1):9-14.PubMedGoogle ScholarCrossref
10.
Iacolucci  CM, Banks  C, Jowett  N,  et al.  Development and validation of a spontaneous smile assay.  JAMA Facial Plast Surg. 2015;17(3):191-196.PubMedGoogle ScholarCrossref
11.
Kahn  JB, Gliklich  RE, Boyev  KP, Stewart  MG, Metson  RB, McKenna  MJ.  Validation of a patient-graded instrument for facial nerve paralysis: the FaCE scale.  Laryngoscope. 2001;111(3):387-398.PubMedGoogle ScholarCrossref
12.
Chu  EA, Farrag  TY, Ishii  LE, Byrne  PJ.  Threshold of visual perception of facial asymmetry in a facial paralysis model.  Arch Facial Plast Surg. 2011;13(1):14-19.PubMedGoogle ScholarCrossref
13.
Dey  JK, Ishii  LE, Byrne  PJ, Boahene  KD, Ishii  M.  Seeing is believing: objectively evaluating the impact of facial reanimation surgery on social perception.  Laryngoscope. 2014;124(11):2489-2497.PubMedGoogle ScholarCrossref
14.
Dey  JK, Ishii  M, Boahene  KD, Byrne  PJ, Ishii  LE.  Changing perception: facial reanimation surgery improves attractiveness and decreases negative facial perception.  Laryngoscope. 2014;124(1):84-90.PubMedGoogle ScholarCrossref
15.
Ishii  L, Godoy  A, Encarnacion  CO, Byrne  PJ, Boahene  KD, Ishii  M.  Not just another face in the crowd: society’s perceptions of facial paralysis.  Laryngoscope. 2012;122(3):533-538.PubMedGoogle ScholarCrossref
16.
Bhama  PK, Weinberg  JS, Lindsay  RW, Hohman  MH, Cheney  ML, Hadlock  TA.  Objective outcomes analysis following microvascular gracilis transfer for facial reanimation: a review of 10 years’ experience.  JAMA Facial Plast Surg. 2014;16(2):85-92.PubMedGoogle ScholarCrossref
17.
Hohman  MH, Hadlock  TA.  Etiology, diagnosis, and management of facial palsy: 2000 patients at a facial nerve center.  Laryngoscope. 2014;124(7):E283-E293.PubMedGoogle ScholarCrossref
18.
Hohman  MH, Kim  SW, Heller  ES, Frigerio  A, Heaton  JT, Hadlock  TA.  Determining the threshold for asymmetry detection in facial expressions.  Laryngoscope. 2014;124(4):860-865.PubMedGoogle ScholarCrossref
19.
Ahmidi  N, Hager  GD, Ishii  L, Fichtinger  G, Gallia  GL, Ishii  M.  Surgical task and skill classification from eye tracking and tool motion in minimally invasive surgery.  Med Image Comput Comput Assist Interv. 2010;13(pt 3):295-302.PubMedGoogle Scholar
20.
Godoy  A, Ishii  M, Byrne  PJ, Boahene  KD, Encarnacion  CO, Ishii  LE.  How facial lesions impact attractiveness and perception: differential effects of size and location.  Laryngoscope. 2011;121(12):2542-2547.PubMedGoogle ScholarCrossref
21.
Ishii  L, Carey  J, Byrne  P, Zee  DS, Ishii  M.  Measuring attentional bias to peripheral facial deformities.  Laryngoscope. 2009;119(3):459-465.PubMedGoogle ScholarCrossref
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