Nonserious Adverse Events in Randomized Trials with Opioid‐Dependent Pregnant Women: Direct versus Indirect Measurement
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: How best to measure the occurrence of adverse events during a randomized clinical trial is an issue that has not been adequately examined in the research literature. Focus of this study was on the examination of the relative frequency of occurrence of adverse events directly recorded during the conduct of the trial compared to an indirect determination of adverse events derived from data collected as part of the trial. METHODS: A secondary analysis of nonserious adverse events that occurred in the Maternal Opioid Treatment: Human Experimental Research (MOTHER) Study was undertaken. MOTHER was a randomized clinical trial of methadone versus buprenorphine in 175 opioid-dependent pregnant women. RESULTS: The two methods of recording adverse events failed to agree on where differences in the frequency of occurrence of adverse events between the medication conditions might exist. Moreover, indirect assessment indicated all participants had experienced at least one adverse event, yet indirect coverage of adverse events was incomplete. CONCLUSIONS: Findings suggest indirect examination of occurrence of adverse events should be cautiously undertaken, because indirect assessment of adverse events makes no distinction between what might be simply typical variation in behavior rather than systematic changes in behavior attributable to study condition, and lacks coverage of the full spectrum of adverse events. SCIENTIFIC SIGNIFICANCE: Contemporaneous direct measurement of adverse events likely yield reasonably valid estimates of the rate of occurrence of the adverse events, while indirect measu-rement of adverse events may not be sufficiently reliable.
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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 | 0.600 | 0.751 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".