Digital assessment of the fetal alcohol syndrome facial phenotype: reliability and agreement study
Bibliographic record
Abstract
PURPOSE: To examine the three facial features of fetal alcohol syndrome (FAS) in a cohort of Australian Aboriginal children from two-dimensional digital facial photographs to: (1) assess intrarater and inter-rater reliability; (2) identify the racial norms with the best fit for this population; and (3) assess agreement with clinician direct measures. METHODS: . Fifty-eight per cent had a confirmed prenatal alcohol exposure and 13 (12%) met the Canadian 2005 criteria for FAS/partial FAS. Photographs were analysed using the FAS Facial Photographic Analysis Software to generate the mean PFL three-point ABC-Score, five-point lip and philtrum ranks and four-point face rank in accordance with the 4-Digit Diagnostic Code. Intrarater and inter-rater reliability of digital ratings was examined in two assessors. Caucasian or African American racial norms for PFL and lip thickness were assessed for best fit; and agreement between digital and direct measurement methods was assessed. RESULTS: Reliability of digital measures was substantial within (kappa: 0.70-1.00) and between assessors (kappa: 0.64-0.89). Clinician and digital ratings showed moderate agreement (kappa: 0.47-0.58). Caucasian PFL norms and the African American Lip-Philtrum Guide 2 provided the best fit for this cohort. CONCLUSION: In an Aboriginal cohort with a high rate of FAS, assessment of facial dysmorphology using digital methods showed substantial inter- and intrarater reliability. Digital measurement of features has high reliability and until data are available from a larger population of Aboriginal children, the African American Lip-Philtrum Guide 2 and Caucasian (Strömland) PFL norms provide the best fit for Australian Aboriginal children.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".