Comparing the Effectiveness of TWEAK and T-ACE in Determining Problem Drinkers in Pregnancy
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it