Incidence of adverse events related to health care in Spain: results of the Spanish National Study of Adverse Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence and incidence density of adverse events (AEs) in Spanish hospitals (including the pre-hospitalisation period). METHOD: Retrospective cohort study. RESULTS: The incidence of patients with AEs relating directly to hospital care was 8.4% (95% CI 7.7% to 9.1%) and rose 9.3% (95% CI 8.6% to 10.1%), including those from the pre-hospitalisation period. The incidence density was 1.2 AEs per 100 patient-days (95% CI 1.1 to 1.3). The incidence of moderate and serious AEs was 5.6 AEs per 1000 patient-days (95% CI 4.9% to 6.3%). In 66.3% of AEs, additional procedures were required and in 69.9% additional treatments were required. In total 42.8% of AEs were considered as avoidable. Of the subjects with some intrinsic risk factors, 13.2% developed AEs compared with 5.2% of the subjects who had no risk factors (p<0.001), and 9.5% of the subjects who had some extrinsic risk factors developed AEs compared with 3.4% of the subjects who had not (p<0.001). Patients older than 65 years of age showed a higher frequency of AEs than those under this age (12.4% vs 5.4%, p<0.001, RR 2.5). The most frequent AEs were those associated with medication (37.4%), hospital infections of any type (25.3%) and those relating to technical problems during a procedure (25.0%). A total of 31.4% of the AEs involved an increase in the length of stay. The AEs associated with medical assistance caused 6.1 additional hospital stays by patient. CONCLUSIONS: The incidence of patients with AE related to medical assistance in Spanish hospitals was relevant and similar to those found in the studies from Canada and New Zealand that had been conducted with comparable methodology. Patient vulnerability has been identified therein as playing a major role in generating healthcare-related AEs. These and other recent results indicate the need for AEs to be considered a public health priority in Europe.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it