Behavioural methods for assessing alcohol dependenceof pregnant women
Bibliographic record
Abstract
Introduction: The problem of alcohol abuse also affects pregnant women, who often conceal the fact that they drink alcohol during pregnancy.The WHO recommends standardized screening tests for diagnosing alcohol abuse and dependence, e.g. the CAGE test, T-ACE test, and TWEAK test.These tests are an easy, non-invasive, quick way to detect pregnant women from the risk group addicted to ethyl alcohol.The aim of the study is to evaluate behavioural methods used to diagnose the alcohol problem of pregnant women, as well as to assess which of the tests (CAGE test, the T-ACE test, or the TWEAK test) is most sensitive and specific in research on the diagnosis of alcoholism among pregnant women in Poland.Results: Based on the survey, it was found that 85% of the respondents drank alcohol before pregnancy, while 26% (39) of the respondents continued to drink alcohol during pregnancy.A positive result of the CAGE test was obtained in 56% of the 39 women who declared that they drank alcohol during pregnancy and in 13% of the respondents who concealed their drinking of alcohol during pregnancy.A positive result of the T-ACE test was obtained in 39% of subjects who admitted to drinking during pregnancy and in 11% of pregnant women who did not admit to it.A positive result of the TWEAK test in the study group was read in 33% of 106 respondents who denied drinking alcohol during pregnancy and in 74% of pregnant women who consumed alcohol.Conclusions: The CAGE, TWEAK, and T-ACE tests differ in sensitivity, specificity, validity, PPV, and NPV; therefore, they should be used to diagnose different alcohol problems.The study of women's alcohol dependence should be based on several tests evaluating alcohol dependence.Implementation of alcohol abuse screening tests in the prenatal care of pregnant women will allow the identification of patients with alcohol problems who do not admit to drinking alcohol.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".