Factors Associated With Prolonged Length of Stay and Failed Lower Limb Prosthetic Fitting During Inpatient Rehabilitation
Bibliographic record
Abstract
To identify variables associated with rehabilitation length of stay (LOS) and prosthetic fitting success for people with lower limb amputation (LLA). Retrospective analysis of clinically collected cohort. Canadian inpatient rehabilitation hospital. Consecutive individuals with LLA (N=103) admitted for prosthetic fitting (mean age, 65.3±10.6y). Not applicable. Independent variables included the Lower Limb Amputee Measurement Scale (LLAMS), which is a 31-question tool to predict LOS with items in medical, cognitive, social, physical, activities of daily living, and other subsections; admission FIM; age; sex; level of amputation (below- or above-knee); and time from surgery to admission. LOS was measured as days from admission to discharge. Successful prosthetic fitting was defined as the ability to use a prosthesis on discharge. The mean LOS was 63.6 ± 33.3 days, and 21.4% of patients failed prosthetic fitting. Higher LLAMS, lower FIM, and above-knee amputation were significantly associated with longer LOS (P<.001, R2=0.36). Age, sex, and time from surgery were not significantly associated with LOS or prosthetic fitting. Higher LLAMS was significantly associated with unsuccessful prosthetic fitting (P=.032). Of the 31 items in the LLAMS, 5 were associated with prolonged LOS and 5 were associated with failed prosthetic fitting (P<.10). The LLAMS, level of amputation, and admission FIM can be used to predict LOS in lower limb amputees admitted for prosthetic fitting. The LLAMS was weak in identifying patients who failed prosthetic fitting. Future research should consider shortening the LLAMS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".