Prevalência e evitabilidade de eventos adversos cirúrgicos em hospital de ensino do Brasil
Bibliographic record
Abstract
OBJECTIVE: to estimate the prevalence and avoidability of surgical adverse events in a teaching hospital and to classify the events according to the type of incident and degree of damage. METHOD: cross-sectional retrospective study carried out in two phases. In phase I, nurses performed a retrospective review on a simple randomized sample of 192 records of adult patients using the Canadian Adverse Events Study form for case tracking. Phase II aimed at confirming the adverse event by an expert committee composed of physicians and nurses. Data were analyzed by univariate descriptive statistics. RESULTS: the prevalence of surgical adverse events was 21.8%. In 52.4% of the cases, detection occurred on outpatient return. Of the 60 cases analyzed, 90% (n = 54) were preventable and more than two thirds resulted in mild to moderate damage. Surgical technical failures contributed in approximately 40% of the cases. There was a prevalence of the infection category associated with health care (50%, n = 30). Adverse events were mostly related to surgical site infection (30%, n = 18), suture dehiscence (16.7%, n = 10) and hematoma/seroma (15%, n = 9). CONCLUSION: the prevalence and avoidability of surgical adverse events are challenges faced by hospital management.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".