Study of a multisite prospective adverse event surveillance system
Bibliographic record
Abstract
BACKGROUND: We have designed a prospective adverse event (AE) surveillance method. We performed this study to evaluate this method's performance in several hospitals simultaneously. OBJECTIVES: To compare AE rates obtained by prospective AE surveillance in different hospitals and to evaluate measurement factors explaining observed variation. METHODS: We conducted a multicentre prospective observational study. Prospective AE surveillance was implemented for 8 weeks on the general medicine wards of five hospitals. To determine if population factors may have influenced results, we performed mixed-effects logistic regression. To determine if surveillance factors may have influenced results, we reassigned observers to different hospitals midway through surveillance period and reallocated a random sample of events to different expert review teams. RESULTS: During 3560 patient days of observation of 1159 patient encounters, we identified 356 AEs (AE risk per encounter=22%). AE risk varied between hospitals ranging from 9.9% of encounters in Hospital D to 35.8% of encounters in Hospital A. AE types and severity were similar between hospitals-the most common types were related to clinical procedures (45%), hospital-acquired infections (21%) and medications (19%). Adjusting for age and comorbid status, we observed an association between hospital and AE risk. We observed variation in observer behaviour and moderate agreement between clinical reviewers, which could have influenced the observed rate difference. CONCLUSION: This study demonstrated that it is possible to implement prospective surveillance in different settings. Such surveillance appears to be better suited to evaluating hospital safety concerns within rather than between hospitals as we could not definitively rule out whether the observed variation in AE risk was due to population or surveillance factors.
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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 | 0.045 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".