Falls in hospital increase length of stay regardless of degree of harm
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Acute inpatient falls are common and serious adverse events that lead to injury, prolonged hospitalization and increased cost of care. To determine the difference in total acute hospital care length of stay (LOS) for patients with and without an in-hospital fall (IHF), regardless of degree of harm. METHODS: This was a retrospective observational study at a 728-bed acute care teaching hospital. We used propensity scores to match 292 patients with 330 controls by case mix group, sex, Resource Intensity Weights and week of admission. We used two administrative databases: hospital fall incident reporting system and Discharge Abstract Database. We reviewed all IHF incidents for patients 18 years and older, admitted to inpatient acute care hospital units/programs between 1 November 2009 and 31 August 2011. RESULTS: The average LOS for IHF cases was 37.2 days [median 26.5 days; interquartile range (IQR) 14, 54] and 25.7 days (median 13 days; IQR 5, 33) for matched control patients. Survival analysis results indicated that patients who did not have an IHF were 2.4 times (95% CI 2.1, 2.7; P < 0.001) more likely to be discharged earlier from acute care than patients who had an IHF. CONCLUSIONS: Experiencing either an injurious or a non-injurious fall during an acute care hospitalization was associated with prolonged LOS.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".