Diagnostic issues affecting the epidemiology of fetal alcohol spectrum disorders.
Bibliographic record
Abstract
BACKGROUND: Epidemiological measures of the prevalence of fetal alcohol spectrum disorders (FASD) vary greatly in the literature. Irrespective of the methodology, the criteria to define a 'case' are set by the researchers. Hence, estimates of the prevalence of FASD primarily depend on the diagnostic criteria currently available. The problem lies therein - the aforementioned criteria are ill-defined. MATERIALS & METHODS: A critical analysis of the diagnostic criteria from the Institute of Medicine, Hoyme, 4-Digit Diagnostic Code and Canadian guidelines was performed, with particular attention focused on the inconsistencies in specificities of the fetal alcohol syndrome (FAS) facial phenotype. RESULTS: To date, the Canadian guidelines represent the only guidelines that have pushed for a uniform diagnostic capacity through harmonizing the IoM and 4-Digit Diagnostic Code criteria. In the absence of a reliable biochemical marker of effect to confirm maternal drinking during pregnancy, the importance and dependence on diagnostic guidelines for FASD is understated. With the availability of four published guidelines for diagnoses across the spectrum of FASD, there is a need to reach a set standard globally. There are profound implications of relaxed and strict diagnostic approaches on FAS prevalence reporting in the literature. CONCLUSIONS: This review exposes the clinical burden of diagnosing the range of FASD with disputing diagnostic criteria. Discrepancies in the criteria pose a danger to the validity of FASD diagnoses with respect to inaccurate estimates of incidence and prevalence. In turn, these discrepancies risk compromising the future healthcare of affected individuals with regards to intervention, counselling and treatment.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".