Neuropsychological validation of a brief quiz to examine comprehension of consent information in observational studies of substance users
Bibliographic record
Abstract
The objective of this study was to determine the accuracy of a brief informed consent quiz (ICQ) to detect consent comprehension in individuals with cognitive impairment (as a proxy of incomprehension) and to explore the degree to which cognitive domains and recent substance use, independently, predict comprehension. We performed a secondary analysis of two cross-sectional studies in individuals with substance use disorders. The ICQ total score was used as the index test and the Montreal Cognitive Assessment (MoCA) as reference standard in receiver operating characteristic curves. Two independent multiple binary logistic regression models were performed using cognitive domains and days of recent substance use as predictors of ICQ outcome. We analyzed data from 215 and 251 participants, respectively. The ICQ showed moderate accuracy for major cognitive impairment (MoCA ≤ 21) (area under the curve ~ 77) and lower accuracy for mild impairment (MoCA ≤ 24) (area under the curve ~ 65). Optimal cutoff score was set at 10 points or less for detecting comprehension difficulty. Lower scores in Short-Term Memory, Attention, Language, and Orientation increased the probability of failing the ICQ. A procedure including both the ICQ and cognitive screening measure could improve the accuracy of consent comprehension assessments.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".