Bringing the patient voice into the operating room: engaging patients in surgical safety research with the Operating Room Black Box®
Bibliographic record
Abstract
Abstract Background Surgery is one of the most common patient experiences in the health care system. Yet, efforts to engage patients in surgical safety research have not matched those of other health care fields. This is a critical issue given the nature of surgery inhibits patients’ abilities to advocate for themselves as they are typically under anesthetic when the procedure is performed. We partnered with patients throughout our research program, which uses the Operating Room Black Box® to enhance surgical patient safety through transparent and proactive analysis of human factors to detect and prevent avoidable errors. Main body In this article, we outline the need for, and our approach to, patient engagement in surgical safety research. Our approach included a series of planned activities and skill development opportunities designed to build capacity and bring together patients, clinicians, and researchers to inform research and practice. We also conducted evaluation surveys during the first year of our program, which have indicated a positive experience by both patient partners and the research team. Conclusion We believe our approach can serve as an important first step toward building a model for patient engagement in the surgical safety field and could significantly contribute to improved quality of care and outcomes for surgical patients.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".