Assessing functional mobility in survivors of lower‐extremity sarcoma: Reliability and validity of a new assessment tool
Bibliographic record
Abstract
BACKGROUND: Reliability and validity of a new tool, Functional Mobility Assessment (FMA), were examined in patients with lower-extremity sarcoma. FMA requires the patients to physically perform the functional mobility measures, unlike patient self-report or clinician administered measures. PROCEDURE: A sample of 114 subjects participated, 20 healthy volunteers and 94 patients with lower-extremity sarcoma after amputation, limb-sparing, or rotationplasty surgery. Reliability of the FMA was examined by three raters testing 20 healthy volunteers and 23 subjects with lower-extremity sarcoma. Concurrent validity was examined using data from 94 subjects with lower-extremity sarcoma who completed the FMA, Musculoskeletal Tumor Society (MSTS), Short-Form 36 (SF-36v2), and Toronto Extremity Salvage Scale (TESS) scores. Construct validity was measured by the ability of the FMA to discriminate between subjects with and without functional mobility deficits. RESULTS: FMA demonstrated excellent reliability (ICC [2,1] >or=0.97). Moderate correlations were found between FMA and SF-36v2 (r = 0.60, P < 0.01), FMA and MSTS (r = 0.68, P < 0.01), and FMA and TESS (r = 0.62, P < 0.01). The patients with lower-extremity sarcoma scored lower on the FMA as compared to healthy controls (P < 0.01). CONCLUSION: The FMA is a reliable and valid functional outcome measure for patients with lower-extremity sarcoma. This study supports the ability of the FMA to discriminate between patients with varying functional abilities and supports the need to include measures of objective functional mobility in examination of patients with lower-extremity sarcoma.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".