Bibliographic record
Abstract
Alcohol is a physical and behavioural teratogen. Fetal alcohol syndrome (FAS) is a common yet under-recognized condition resulting from maternal consumption of alcohol during pregnancy. While preventable, FAS is also disabling.Although FAS is found in all socioeconomic groups in Canada, it has been observed at high prevalence in select First Nations and Inuit communities in Canada.This statement addresses FAS prevention, diagnosis, early identification and management for health care professionals.Prevention of FAS must occur at two levels. Primary prevention involves eliminating FAS through classroom or community education, and encouraging women to avoid consuming alcohol before conception and throughout pregnancy. Secondary prevention involves identifying women who are drinking while pregnant and reducing their consumption. This statement describes a variety of screening strategies including Tolerance-Annoyance, Cut Down, Eye Opener (T-ACE). Medical practitioners should recommend abstinence starting with the first prenatal visit. Prompt referral for alcohol treatment is recommended for pregnant individuals who are unable to stop drinking alcohol.This statement describes the diagnosis of FAS, partial or atypical FAS, alcohol-related birth defects and alcohol-related neurodevelopmental disorder. With a history of in-utero alcohol exposure, a diagnosis of FAS should be considered with current or previous growth deficiency, select facial abnormalities involving the upper lip and eyes, and neurodevelopmental abnormalities. These features are best quantified with the use of a four-digit diagnostic method.Strategies for early identification of possible alcohol-related abnormalities are outlined.Intervention focuses on optimizing development, managing behavioural difficulties and providing appropriate school programming. Of prime importance is earliest possible childhood intervention to prevent secondary disabilities that may result from delay while awaiting a definitive diagnosis of FAS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".