The prevalence of potentially preventable deaths in an acute care hospital
Bibliographic record
Abstract
Studies estimate that 6% to 27% of deaths in hospitals might be prevented with higher quality care. These estimates may be inaccurate because they fail to account for the uncertainty associated with classifying preventability. The purpose of this study was to measure the prevalence of preventable deaths, accounting for the uncertainty in preventability ratings.We created standardized structured case abstracts for all deaths at a multisite academic teaching hospital over a 3-month period. Each case abstract was evaluated independently by 4 reviewers who rated death preventability on a 100-point scale ranging from 0 ("Definitely not preventable") to 100 ("Definitely preventable"). Ratings were categorized into a 4-level ordinal scale and latent class analysis was used to measure the prevalence of each preventability class and estimate the probability that deaths in each class were preventable.There were 480 deaths (3.4% of all admissions) during the study period. The latent class model (LCM) found that 91.6% (95% CI: 88.4-94.8%) of deaths were "nonpreventable" and 8.4% (5.2-11.6%) were "possibly preventable." "Possibly preventable" deaths could be identified with 90% certainty, but due to error in reviewer ratings, a "possibly preventable" death had a 50% probability of being receiving a rating of less than 25/100 by any single reviewer. Only 5 of 31 deaths classified as a "possibly preventable" (1.0% of all deaths) were judged to likely be alive in 3 months with perfect care.After accounting for uncertainty associated with rating the preventability of hospital deaths, we found that 8.4% of deaths were deemed possibly preventable. There was only moderate probability that these deaths were truly preventable.
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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 | 0.034 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| 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".