Surface Anatomy of the Face in Down's Syndrome: Linear and Angular Measurements in the Craniofacial Regions
Bibliographic record
Abstract
Measurements (23 projective linear, 2 angular) taken in the 6 craniofacial regions of 127 patients with Down's syndrome showed that 63.1% (1,836 of 2,908) were within normal limits and 36.9% (1,072) were outside them. Abnormal measurements were subnormal in 90.8% (973) and supernormal in 9.2% (99). All statistical summaries were based on z scores (adjusting for age and sex differences) classified into a small number of ranges to yield a simplified frequency distribution for each measurement. The purpose of the study was to identify the measurements closest to normal and those indicating the most severe degrees of sub- or supernormality. Approximately a quarter of normal measurements were classified as optimal, and half the subnormal or supernormal measurements were classified as severe. Intercanthal width had the highest frequency of optimal measurements (93.7%, 119 of 127), head circumference the smallest (28.6%, 36 of 126). Knowledge of the frequency of extreme abnormalities in the craniofacial regions will help during visual examination of patients with Down's syndrome. This study found the highest percentage of severely subnormal measurements in the orbital region (57.8%, 74 of 128) and the smallest in the labio-oral region (32.7%, 16 of 49). The measurement with the highest proportion of severely subnormal to all subnormal values was the palpebral fissure length (68%, 51 of 75), and the nose width had the smallest proportion (14.3%, 1 of 7).
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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 | 0.000 | 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".