Monitoring adverse events in older hospitalized patients: A retrospective cross-sectional study using validated screening criteria with administrative data
Bibliographic record
Abstract
Objective: Older patients are at higher risk of experiencing an adverse event (AE) during an acute hospitalization. The objective of the current study was to use routinely collected administrative data to characterize AEs and their system-level impact for older patients hospitalized in one Canadian health authority.Methods: This retrospective cross-sectional study occurred in the Capital District Health Authority in Nova Scotia, Canada between April 1, 2012 and March 31, 2013. The primary outcome was identification of pressure ulcers, fall-related injuries and adverse drug events in patients 65 years of age and older admitted to an acute inpatient service. AEs were identified using validated screening criteria. Data were analyzed using standard descriptive statistics.Results: There were 11,747 hospitalizations during the study period. A total of 330 (2.8%) AEs in 325 patients were identified using the screening criteria. This included 55 (16.7% of 330) pressure ulcers, 25 (7.6%) fall-related injuries and 250 (75.8%) adverse drug events. The average length of stay was significantly higher in patients with a pressure ulcer (35.8 ± 47.3 vs. 9.0 ± 14.8 days, p < .0001), fall-related injury (30.3 ± 23.2 vs. 9.0 ± 15.2 days, p < .0001), or adverse drug event (14.6 ± 14.4 vs. 9.0 ± 15.2 days, p < .0001) during their acute hospitalization.Conclusions:Use of validated screening criteria with administrative hospitalization data provides important information for monitoring the system-level impact of common AEs in older patients. Significant and clinically important differences in healthcare utilization underscore the value in monitoring these AEs in this growing patient population.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".