Incidence and Nature of Adverse Events During Inpatient Rehabilitation
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to describe and compare adverse event incidence, type, severity, and preventability in the Canadian inpatient rehabilitation setting. DESIGN: In this retrospective case series, adverse events were identified through chart reviews from two Canadian academic tertiary postacute care hospitals. Adverse events were characterized through descriptive statistics and compared using the Mantel-Haenszel and Fisher's exact tests. RESULTS: During the study period, one site ( n = 120) had 28 adverse events and an incidence of 9.7 (95% CI = 6.1-13.3) per 1000 patient days, and the other ( n = 48) had 15 adverse events and an incidence of 13.9 (95% CI = 6.9-21) per 1000 patient days ( P = 0.82). The two sites differed significantly in adverse event type ( P = 0.033) and preventability ( P = 0.002) but not severity. The most common adverse event type was medication/intravenous fluids-related (16/28, 57%) at one site and patient incidents (e.g., falls, pressure ulcers) at the other. Four percent (1/28) of adverse events were preventable at one site, and 53% (8/15) at another. Most adverse events at both sites were mild in severity. CONCLUSIONS: Adverse events significantly differed in type and preventability between the two sites. These results suggest the importance of context and the need for an organization-specific and tailored approach when addressing patient safety in inpatient rehabilitation settings.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".