A Multicenter Cohort Study of Falls Among Patients Admitted to the ICU*
Bibliographic record
Abstract
OBJECTIVES: To determine the incidence of falls, risk factors, and adverse outcomes, among patients admitted to the ICU. DESIGN: Retrospective cohort study. SETTING: Seventeen ICUs in Alberta, Canada. PATIENTS: Seventy-three thousand four hundred ninety-five consecutive adult patient admissions between January 1, 2014, and December 31, 2019. MEASUREMENTS AND MAIN RESULTS: A mixed-effects negative binomial regression model was used to examine risk factors associated with falls. Linear and logistic regression models were used to evaluate adverse outcomes. Six hundred forty patients experienced 710 falls over 398,223 patient days (incidence rate of 1.78 falls per 1,000 patient days [95% CI, 1.65-1.91]). The daily incidence of falls increased during the ICU stay (e.g., day 1 vs day 7; 0.51 vs 2.43 falls per 1,000 patient days) and varied significantly between ICUs (range, 0.37-4.64 falls per 1,000 patient days). Male sex (incidence rate ratio [IRR], 1.37; 95% CI, 1.15-1.63), previous invasive mechanical ventilation (IRR, 1.82; 95% CI, 1.40-2.38), previous sedative and analgesic medication infusions (IRR, 1.60; 95% CI, 1.15-2.24), delirium (IRR, 3.85; 95% CI, 3.23-4.58), and patient mobilization (IRR, 1.26; 95% CI, 1.21-1.30) were risk factors for falling. Falls were associated with longer ICU (ratio of means [RM], 3.10; 95% CI, 2.86-3.36) and hospital (RM, 2.21; 95% CI, 2.01-2.42) stays, but lower odds of death in the ICU (odds ratio [OR], 0.09; 95% CI, 0.05-0.17) and hospital (OR, 0.21; 95% CI, 0.14-0.30). CONCLUSIONS: We observed that among ICU patients, falls occur frequently, vary substantially between ICUs, and are associated with modifiable risk factors, longer ICU and hospital stays, and lower risk of death. Our study suggests that fall prevention strategies should be considered for critically ill patients admitted to ICU.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".