Bibliographic record
Abstract
Background: As part of an interdisciplinary study of medical injury and malpractice litigation, we estimated the incidence of adverse events, defined as injuries caused by medical management, and of the subgroup of such injuries that resulted from negligent or substandard care.Methods: We reviewed 30 121 randomly selected records from 51 randomly selected acute care, nonpsychiatric hospitals in New York State in 1984.We then developed population estimates of injuries and computed rates according to the age and sex of the patients as well as the specialties of the physicians.Results: Adverse events occurred in 3.7% of the hospitalizations (95% confidence interval 3.2 to 4.2), and 27.6% of the adverse events were due to negligence (95% confidence interval 22.5 to 32.6).Although 70.5% of the adverse events gave rise to disability lasting less than 6 months, 2.6% caused permanently disabling injuries and 13.6% led to death.The percentage of adverse events attributable to negligence increased in the categories of more severe injuries (Wald test x 2 = 21.04,p,0.0001).Using weighted totals we estimated that among the 2 671 863 patients discharged from New York hospitals in 1984 there were 98 609 adverse events and 27 179 adverse events involving negligence.Rates of adverse events rose with age (p,0.0001).The percentage of adverse events due to negligence was markedly higher among the elderly (p,0.01).There were significant differences in rates of adverse events among categories of clinical specialties (p,0.0001),but no differences in the percentage due to negligence.Conclusions: There is a substantial amount of injury to patients from medical management, and many injuries are the result of substandard care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.125 | 0.021 |
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".