Expanding the Classic Facial Canons: Quantifying Intercanthal Distance in a Diverse Patient Population
Bibliographic record
Abstract
Background: The intercanthal distance (ICD) is central to our perception of facial proportions, and it varies according to gender and ethnicity. Current standardized reference values do not reflect the diversity among patients. Therefore, the authors sought to provide an evidence-based and gender/ethnicity-specific reference when evaluating patients’ ICD. Methods: As per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search of PubMed, Medline, and Embase was carried out for studies reporting on the ICD. Demographics, study characteristics, and ICDs were extracted from included studies. ICD values were then pooled for each ethnicity and stratified by gender. The difference between men and women, and that across ethnicities and measurement types were compared by means of independent sample t-test and one-way ANOVA (SPSS v.24). Results: A total of 67 studies accounting for 22,638 patients and 118 ethnic cohorts were included in this pooled analysis. The most reported ethnicities were Middle Eastern (n = 6629) and Asian (n = 5473). ICD values (mm) in decreasing order were: African 38.5 ± 3.2, Asian 36.4 ± 1.6, Southeast Asian 32.8 ± 2.0, Hispanic 32.3 ± 2.0, White 31.4 ± 2.5, and Middle Eastern 31.2 ± 1.5. A statistically significant difference (P < 0.05) existed between all ethnic cohorts, between genders among most cohorts, and between most values stratified by measurement type. Conclusions: Our standards of craniofacial anthropometry must evolve from the neoclassical canons using White values as references. The values provided in this review can aid surgeons in appreciating the gender- and ethnic-specific differences in the ICD of their patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".