Validation of the facial photographic in fetal alcohol spectrum disorder screening and diagnosis.
Bibliographic record
Abstract
OBJECTIVE: A prospective study to validate the computer-assisted method of measuring palpebral fissure length and philtrum smoothness using digital patient photographs. These are key diagnostic facial features of Fetal Alcohol Syndrome. PARTICIPANTS: Motherisk Program (including Breaking the Cycle), Hospital for Sick Children, Toronto - a clinical, research and teaching program dedicated to antenatal drug, chemical, and disease risk counseling. 40 children referred for FASD assessment, 21 under 4 years old, 19 were 4 years or older. METHODS/ MATERIALS: Facial measurements were obtained directly from the patient by physicians and compared to those obtained by computer software measurement of photographs of the same patient. OUTCOME MEASURES: Palpebral fissure length and philtrum smoothness. RESULTS: The photographic measurements showed shorter palpebral fissure length than the direct measurements when analyzing all children (25.4±2.3 vs .23.2±2.4mm; p<0.0001), and children under four (n=21, 24.7±2.4 vs. 21.6±1.6mm; p<0.0001). The difference for older children (n=19) did not reach statistical significance. The computer found four false positive cases and no false negative cases of clinically short palpebral fissure (sensitivity=100%, specificity=64%). Direct measurement scores for philtrum smoothness were different from the computer's measurements using the frontal view (p=0.0012) but not using the ¾ view. CONCLUSION: The method of computer-assisted measurement tends to underestimate the true length and, hence, over- diagnose short palpebral fissure, especially in children under four years old. This method may serve as a useful fetal alcohol syndrome screening tool.
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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 | 0.005 | 0.015 |
| 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".