What Research Is Being Done on Prenatal Alcohol Exposure and Fetal Alcohol Spectrum Disorders in the Russian Research Community?
Bibliographic record
Abstract
AIMS: Although Russia has one of the highest rates of alcohol consumption and alcohol-attributable burden of disease, little is known about the existing research on prenatal alcohol exposure (PAE) and Fetal Alcohol Spectrum Disorders (FASDs) in this country. The objective of this study was to locate and review published and unpublished studies related to any aspect of PAE and FASD conducted in or using study populations from Russia. METHODS: A systematic literature search was conducted in multiple English and Russian electronic bibliographic databases. In addition, a manual search was conducted in several major libraries in Moscow. RESULTS: The search revealed a small pool of existing research studies related to PAE and/or FASD in Russia (126: 22 in English and 104 in Russian). Existing epidemiological data indicate a high prevalence of PAE and FASD, which underlines the strong negative impact that alcohol has on mortality, morbidity and disability in Russia. High levels of alcohol consumption by women of childbearing age, low levels of contraception use, and low levels of knowledge by health and other professionals regarding the harmful effects of PAE put this country at great risk of further alcohol-affected pregnancies. CONCLUSIONS: Alcohol preventive measures in Russia warrant immediate attention. More research focused on alcohol prevention and policy is needed in order to reduce alcohol-related harm, especially in the field of FASD.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".