162
Bibliographic record
Abstract
Introduction: To ensure the rights and safety of research participants, protocols for quality improvement studies are typically reviewed by Research Ethics Boards (REBs) before commencement. We previously found that the median time to REB approval was approximately 2.5 months (IQR 1.5, 5 months) for a low risk randomized trial. The objectives of this study were to analyze time from REB submission to REB approval, time from contract proposal to approval, and predictors of these periods, for a one month retrospective audit. Hypothesis: We hypothesized that time to REB approval would be longer in smaller ICUs and hospitals, ICUs without national quality improvement experience, centers requiring full REB review, a paper submission process, and French application. Methods: We analyzed the process and outcomes of individual REB applications and contracts for a one month retrospective audit of thromboprophylaxis prescribing in 28 North American medical-surgical ICUs [CONECCKT-T (Co-operative Network of Critical Care Knowledge Translation- Thromboprophylaxis)]. Results: CONECCKT-T approval was granted by 22 unique REBs; all but 2 applications were approved without revisions. Of 22 applications, 14 (63.6%) were expedited, 8 (36.4%) were electronic rather than paper-based, and 5 (22.7%) were in French. The time to REB approval was 21.5 (9-44) days [median (interquartile range)]; the only feature associated with shorter time to approval was expedited rather than regular submission (12.5 vs 27.5 days, p=0.02). Contract initiation to full execution took 74 (57-89) days; no factors predicted shorter time to execution. Conclusions: For a one month retrospective audit with waived informed consent, time for contract execution was more than three times as long (10.5 weeks) as time to REB approval (3 weeks). To encourage quality improvement initiatives using low risk study designs in the ICU, more efficient, cost-effective document management processes are needed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.539 | 0.407 |
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".