A post-discharge functional outcome measure for lower limb amputees
Bibliographic record
Abstract
There are approximately 700 lower limb amputations performed throughout Scotland each year. A national system of survey and analysis conducted by the Scottish Physiotherapy Amputee Research Group (SPARG) provides information on these patients up until discharge from hospital. However, there has been no method of collecting long-term functional and prosthetic use information following discharge. The Functional Measure for Amputees (FMA) has, therefore, been developed from the Prosthetic Profile of the Amputee (PPA) questionnaire, designed by Gauthier-Gagnon and colleagues in Canada (Grisé et al, 1993). Modifications to the PPA were carried out to make it more appropriate for the Scottish amputee population; these changes were approved by the original authors. The test-retest reliability of the 14-question FMA was assessed using a repeat postal questionnaire study. One hundred and thirty-three (133) from a possible 390 trans-tibial amputees were returned. Comparing sociodemographic and clinical variables between consenters and non-consenters showed no evidence to support sample bias. Continuous data items on the FMA analysed using an intraclass correlation coefficient showed ICC values of 0.74, 0.85, 0.96 and 0.64. Categorical data items analysed using percentage agreements showed reliability of over 70% for seven items, between 40% and 70% for three items and between 20% and 40% for the remaining three items. The FMA questionnaire was found to be reliable on the majority of its questions and moderately reliable on the remaining questions during successive follow-up postal administrations.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".