Falls Sustained During Inpatient Rehabilitation After Lower Limb Amputation
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to identify risk factors for falling and fall-related injury among a group of inpatients undergoing rehabilitation after major lower limb amputation. DESIGN: Retrospective cohort. RESULTS: Out of 1267 patients, 260 (20.5%) fell at least once. There were a total of 374 falls, 67 (17.9%) of which resulted in one or more injuries. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated for factors significantly associated with falling, including age of > or =71 yrs (OR = 1.40, 95% CI = 1.02-1.89), lengths of stay of 22-35 days (OR = 2.97, 95% CI = 1.14-7.72) or >5 wks (OR = 6.07, 95% CI = 2.34-15.71), four or more comorbidities (OR = 1.93, 95% CI = 1.09-3.41), cognitive impairment (OR = 1.68, 95% CI = 1.02-2.78), two or more as-needed medications (OR = 1.81, 95% CI = 1.02-3.21), benzodiazepines (OR = 2.22, 95% CI = 1.24-3.96), and opiates (OR = 5.76, 95% CI = 3.29-10.09). Factors significantly associated with fall-related injuries included bilateral amputation (OR = 3.68, 95% CI = 1.49-9.05) and falls during the day shift (OR = 2.63, 95% CI = 1.24-5.57). CONCLUSIONS: One in five patients with lower limb amputation will likely experience at least one fall during inpatient rehabilitation, with 18% sustaining an injury. Ongoing research is required to develop appropriate intervention strategies to ameliorate the risk of falling during inpatient rehabilitation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".