Assessment of the incidence and preventability of adverse events in hospitals: an integrative review
Bibliographic record
Abstract
OBJECTIVE: To highlight the scientific production related to the use of the retrospective chart review methods to assess the incidence and preventability of adverse events in hospitals. METHOD: An integrative review in the MEDLINE, LILACS, SCOPUS, Web of Science and EMBASE databases conducted in May 2019 with the following guiding question: What is known about the retrospective chart review methods to assess the incidence and preventability of adverse events in hospitals? Subsequently, the categorization, synthesis, and classification of the evidence levels of the included publications were performed. RESULTS: In the 13 selected studies, the instruments adopted to assess the occurrence of adverse events were the Harvard Medical Practice Study, the Canadian Adverse Event Study, the Quality in Australian Health Care Study, and the Global Trigger Tool. Incidence ranged from 5.7 to 14.2%, while preventability ranged from 31 to 83%. CONCLUSION: Differences in incidence and preventability were found, showing different results in the quality of care provided, the information registered in medical records, the screening criteria used, and the assessments of the reviewers.
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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 | 0.023 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".