Comparison of the Morphology of the “Cleft Face” and the Normal Face: Defining the Anthropometric Differences
Bibliographic record
Abstract
Morphological characteristics of the "cleft face" were analyzed by calculating proportion indices in young adults who had undergone surgery in childhood for cleft lip and/or palate: 592 in 37 subjects with unilateral clefts and 432 in 27 with bilateral clefts. Three areas of the face with 16 indices based on 12 projective linear measurements were analyzed in each subject: 5 indices in the general face, 6 in the upper face, and 5 in the lower. Although a balanced relationship was found in two thirds of the indices assessed, severe disproportions, which greatly influence appearance, were noted in about one quarter. In the general face, the most frequent abnormal anthropometric findings contributing to severe disproportions were small upper-face height and a severely high or moderately narrow mandible. In the soft tissues, disproportions included wide nose, small nasal tip protrusion, and short or long columella. Deviations from normality were rarely noted in the nose and upper-lip height, mouth width, total facial height, and width of the upper face. In the upper face, severe disproportions occurred much more frequently in bilateral cleft subjects (67.6%) than in unilateral clefts (30.3%). In the lower face, however, severe disproportions were twice as frequent among unilateral clefts (39.4%) than bilateral (14.7%). The results, although interesting, require complementary preoperative data for reliable analysis of the adult "cleft face."
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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) | 0.002 | 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".