Adverse events among medical patients after discharge from hospital.
Bibliographic record
Abstract
BACKGROUND: Adverse events (AEs) are adverse outcomes caused by medical care. Several studies have indicated that a substantial number of patients experience AEs before or during hospitalization. However, few data describe AEs after hospital discharge. We determined the incidence, severity, preventability and ameliorability of AEs in patients discharged from the general internal medicine service of a Canadian hospital. METHODS: At a multisite Canadian teaching hospital, we prospectively studied patients who were consecutively discharged home or to a seniors' residence from the general internal medicine service during a 14-week interval in 2002. We used telephone interview and chart review to identify outcomes after discharge. Two physicians independently reviewed each outcome to determine if the patient experienced an AE. The severity, preventability and ameliorability of all AEs were classified. RESULTS: During the study period, outcomes were determined for 328 of the 361 eligible patients, who averaged 71 years of age (interquartile range 54-81 years). After discharge, 76 of the 328 patients experienced at least 1 AE (overall incidence 23%, 95% confidence interval [CI] 19%-28%). The AE severity ranged from symptoms only (68% of the AEs) or symptoms associated with a nonpermanent disability (25%) to permanent disability (3%) or death (3%). The most common AEs were adverse drug events (72%), therapeutic errors (16%) and nosocomial infections (11%). Of the 76 patients, 38 had an AE that was either preventable or ameliorable (overall incidence 12%, 95% CI 9%-16%). INTERPRETATION: Approximately one-quarter of patients in our study had an AE after hospital discharge, and half of the AEs were preventable or ameliorable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".