Comparing the Effectiveness of TWEAK and T-ACE in Determining Problem Drinkers in Pregnancy
Bibliographic record
Abstract
AIM: The TWEAK and T-ACE screening tools are validated methods of identifying problem drinking in a pregnant population. The objective of this study was to compare the effectiveness of the TWEAK and T-ACE screening tools in identifying problem drinking using traditional cut-points (CP). METHODS: Study participants consisted of women calling the Motherisk Alcohol Helpline for information regarding their alcohol use in pregnancy. In this cohort, concerns surrounding underreporting are not likely as women self-report their alcohol consumption. Participant's self-identification, confirmed by her amount of alcohol use, determined whether she was a problem drinker or not. The TWEAK and T-ACE tools were administered on both groups and subsequent analysis was done to determine if one tool was more effective in predicting problem drinking. RESULTS: The study consisted of 75 problem and 100 non-problem drinkers. Using traditional CP, the TWEAK and T-ACE tools both performed similarly at identifying potential at-risk women (positive predictive value = 0.54), with very high sensitivity rates (100-99% and 100-93%, respectively) but poor specificity rates (36-43% and 19-34%, respectively). Upon comparison, there was no statistical difference in the effectiveness for one test performing better than next using either CP of 2 (P = 0.66) or CP of 3 (P = 0.38). CONCLUSION: Despite the lack of difference in performance, improved specificity associated with TWEAK suggests that it may be better suited to screen at-risk populations seeking advice from a helpline.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".