Development and preliminary validation of an indirect screener for drug use in the perinatal period
Bibliographic record
Abstract
AIMS: This study sought to develop and begin validation of an indirect screener for identification of drug use during pregnancy, without reliance on direct disclosure. DESIGN: Women were recruited from their hospital rooms after giving birth. Participation involved (i) completing a computerized assessment battery containing three types of items: direct (asking directly about drug use), semi-indirect (asking only about drug use prior to pregnancy) and indirect (with no mention of drug use), and (ii) providing urine and hair samples. An optimal subset of indirect items was developed and cross-validated based on ability to predict urine/hair test results. SETTING: Obstetric unit of a university-affiliated hospital in Detroit. PARTICIPANTS: Four hundred low-income, African American, post-partum women (300 in the developmental sample and 100 in the cross-validation sample); all available women were recruited without consideration of substance abuse risk or other characteristics. MEASUREMENTS: Women first completed the series of direct and indirect items using a Tablet PC; they were then asked for separate consent to obtain urine and hair samples that were tested for evidence of illicit drug use. FINDINGS: In the cross-validation sample, the brief screener consisting of six indirect items predicted toxicology results more accurately than direct questions about drug use (area under the ROC curve = 0.74, P < 0.001). Traditional direct screening questions were highly specific, but identified only a small minority of women who used drugs during the last trimester of pregnancy. CONCLUSIONS: Indirect screening may increase the accuracy of mothers' self-reports of prenatal drug use.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".