Selection of outcome measures in lower extremity amputation rehabilitation: ICF activities
Bibliographic record
Abstract
PURPOSE: To identify and evaluate the lower extremity amputee (LEA) rehabilitation outcome measurement instruments that quantify those outcomes that have been classified within the ICF category of activities. This was done to assist the clinicians in the selection of the most appropriate instrument based upon four determinants of successful LEA rehabilitation and outcome measurement. METHOD: A systematic review of the literature associated with outcome measurement in LEA rehabilitation was conducted. Only articles containing data related to metric properties (reliability, validity or responsiveness) for an instrument were included. Articles were identified by electronic and hand-searching techniques and were subsequently classified first according to the ICF and then by their clinical use. RESULTS: Seventeen instruments were identified that were classified into one of (A) walk tests, (B) mobility grades and (C) indices (generic and amputee-specific). Evidence about metric properties and clinical utility was summarised in tables which formed the basis for conclusions and recommendations pertaining to LEA rehabilitation. CONCLUSIONS: All instruments examined have the potential for some use within the initial rehabilitation trial following amputation. There is a universal absence of quality evidence demonstrating responsiveness and most instruments would benefit from further investigation to better define their optimal use.
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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 | 0.029 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".