Study protocol for a Prospective Observational study of Safety Threats and Adverse events in Trauma (PrO-STAT): a pilot study at a level-1 trauma centre in Canada
Bibliographic record
Abstract
INTRODUCTION: Traumatic injuries are a significant public health concern globally, resulting in substantial mortality, hospitalisation and healthcare burden. Despite the establishment of specialised trauma centres, there remains considerable variability in trauma-care practices and outcomes, particularly in the initial phase of trauma resuscitation in the trauma bay. This stage is prone to preventable errors leading to adverse events (AEs) that can impact patient outcomes. Prior studies have identified common causes of these errors, including delayed diagnostics, disorganisation of staff, equipment issues and communication breakdowns, which collectively contribute to AEs. This study addresses gaps in understanding the root causes of these errors by evaluating the most frequent AEs in trauma care through real-time video reviews of resuscitations in the trauma bay. Insights from this evaluation will inform targeted interventions to improve procedural adherence, communication and overall team performance, ultimately reducing preventable errors and improving patient safety. METHODS AND ANALYSIS: A prospective observational study will be conducted at St. Michael's Hospital, a level-1 trauma centre, to evaluate resuscitations in the trauma bay. All consecutive trauma team activations over 12 months will be included, with data collected using audio-visual recordings and physiological monitoring. A synchronised data capture and analysis platform will comprehensively assess AEs, errors and human and environmental factors during trauma resuscitations. The study aims to detect recurring error patterns, evaluate practice variations and correlate trauma team performance with in-hospital outcomes. Statistical analyses will include descriptive statistics, logistic regression models and multivariable analyses to identify associations and predictors of AEs and patient outcomes. ETHICS AND DISSEMINATION: Institutional research ethics approval was obtained (SMH REB # 21-009). A modified consent model will be employed for participants. Staff, physicians and learners will be provided with information regarding the study and will have the option to opt-out or withdraw consent. Similarly, trauma patients and their next of kin will be informed about the study, with provisions for opting out or withdrawing consent within 48 hours of recording. Measures will be implemented to ensure data confidentiality, anonymity and respect for participants' autonomy and privacy. The study results will be shared through peer-reviewed journal publications and conference presentations, and key institutional stakeholders will be informed about developing strategies to improve patient safety in trauma care.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".