An eight-year analysis of participant characteristics at admission to inpatient prosthetic rehabilitation following a lower limb amputation: a Canadian perspective
Bibliographic record
Abstract
PURPOSE: To describe admission and discharge characteristics of participants admitted to prosthetic rehabilitation following a lower limb amputation and determine changes in participant characteristics including if the population has gotten older over time at admission. METHODS: A retrospective chart audit of consecutive admissions to an amputee rehabilitation program. Study criteria were transtibial level LLA and above and ≥ 18 years old. Admission characteristics included: age, Montreal Cognitive Assessment (MoCA), Functional Comorbidity Index (FCI) and days between amputation surgery and admission. Discharge characteristics included the L -Test of Functional Mobility (L-Test), 2-Minute Walk Test (2MWT), 6-Minute Walk Test (6MWT), and Activities-specific Balance Confidence (ABC) scale. Multivariable linear regression modelling quantified the association between participant characteristics and admission time. RESULTS: = 0.011] were independently associated with admission time. CONCLUSION: People with an LLA are presenting with a higher number of comorbidities at admission over time while being admitted faster from amputation surgery. Future research should investigate the impact of these changing characteristics on rehabilitation outcomes to better assist this 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".