Fetal alcohol spectrum disorder: counting the invisible - mission impossible?
Bibliographic record
Abstract
The article by Elliott et al in this issue raises many questions about how best to identify children who are affected by prenatal exposure to alcohol.1 This prospective, active case-finding national surveillance study in Australia showed the very low rate (0.58 per 105 children aged <15 years per annum) of the visible subset of children diagnosed by paediatricians with fetal alcohol syndrome (FAS). The authors considered the likelihood that there had been under-reporting of the syndrome due to several factors, including difficulty in making, or lacking in skills to make, a diagnosis, lack of awareness and recognition by physicians in considering the diagnosis, lack of reporting, lack of availability of specialists in high risk and remote areas, and paediatricians not being prepared to deal with a FAS diagnosis. This is not the first time that investigators have considered the possibility that many children with fetal alcohol spectrum disorder (FASD) are missed.2 Studies in other parts of the world suggest a much higher rate and prevalence. In a high participation county from a school age population study in Washington state, the minimal prevalence of full-blown FAS was 3.1 per 1000.3 In the USA, the best estimates for the whole spectrum of affected children with FASD suggest a prevalence approaching 1%.4 In Western Canada, the rates in selected communities and regions showed …
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".