Incidence and prevalence of fetal alcohol spectrum disorder by sex and age group in Alberta, Canada.
Bibliographic record
Abstract
OBJECTIVES: To estimate incidence and prevalence of FASD by sex and age in Alberta, Canada. METHODS: We included all patients recorded in the Alberta provincial health databases of inpatients, outpatients, and practitioner claims from 2003 to 2012. The number of people with FASD were calculated from available data on FAS (ICD-9 code 760.71; ICD-10 codes Q86.0 and P04.3) and estimated prevalence of FASD among individuals diagnosed with 21 FASD-related conditions (identified by a literature review) for which there are ICD codes, such as learning disability, mental retardation, and nervous system defects (Table 1). Fractions of FASD-related diagnoses that can be attributed to alcohol use during pregnancy were estimated by a systematic review. The incidence was measured as the number of new cases per 1000 births. The prevalence was measured as the number of cases per 1000 population in 2012. RESULTS: Annually, 739 to 1884 people were born with FASD in Alberta establishing an incidence of 14.2 to 43.8 per 1000 births, depending on the length of follow-up. There were about 46,000 people living with FASD in Alberta 2012, including 6,000 FAS cases and 40,000 FASD-related cases. The prevalence of FASD was 11.7 (range 8.2 to 15.1) per 1000 population. The incidence and prevalence varied greatly by sex and age group. Generally, male and younger outnumbered female and older. CONCLUSION: This study suggests new incidence and prevalence of FASD, which are higher than what has been commonly used (1%), and its variations among sex and age groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".