Outcome measures in amputation rehabilitation: ICF body functions
Bibliographic record
Abstract
PURPOSE: To identify and evaluate the lower limb amputation rehabilitation outcome measurement instruments that quantify those outcomes classified within the International classification of functioning, disability and health (ICF) category of body function or structure. This was done to summarise the current evidence base for the most commonly used outcome measurement tools and to provide clinicians with recommendations on how specific tools might be selected for use. METHOD: A systematic review of the literature associated with outcome measurement in lower limb amputation 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 according to the ICF. RESULTS: Sixteen instruments were identified that were classified into one of Global mental function (12), Sensory and pain (1), Cardiovascular and respiratory (1) and Neuromusculoskeletal and movement (2). Evidence about metric properties and clinical utility was summarised in tables, which formed the basis for conclusions. CONCLUSIONS: Few well-validated body function tools exist in the amputee literature, which may explain their lack of widespread use. For all scales, responsiveness to intervention has not been well established and should be the focus of future studies along with continued establishment of validity and reliability.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".