Anthropometric and Anthroposcopic Analysis of Periorbital Features in Malaysian Population: An Inter-racial Study
Bibliographic record
Abstract
Abstract In oculoplastic operations, knowledge of the dimensions of periorbital features based on age, gender, and race is essential for achieving better aesthetic result. This article seeks to determine the racial and gender differences of periorbital features among Malaysian Malay (MM), Malaysian Indian (MI), and Malaysian Chinese (MI) subjects. Evaluation of periorbital features was done on photographs of 200 MM, 200 MI, and 200 MC subjects, aged 18 to 26 years. The measured values were evaluated by an independent t-test. A significant difference was found between MM and MI in all measurements except interbrow distance in males, eyebrow thickness in females, and apex to lateral limbus distance in both sexes. Between MI and MC the difference was insignificant for interbrow distance in male groups, apex to lateral limbus distance in females, and palpebral fissure inclination and eyebrow apex angle in both sexes. Between MM and MC, significant differences were found for eyebrow thickness and medial canthus tilt in female group. Male groups showed significant difference for apex to lateral limbus and lateral canthus distance and eyebrow apex angle. Eyebrow height, palpebral fissure width, and intercanthal distance were significantly different in both sexes. Sexual dimorphism was found for all measurements in MI, but MM and MC showed insignificant difference for eyebrow apex angle. Four types of epicanthus were observed in MM and MC and three types in MI. Eyebrow apex between lateral limbus and lateral canthus was the most common position in all racial groups. Significant racial and gender differences exist for certain periorbital measurements. The knowledge of these differences is expected to influence the surgical outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".