Evaluation of the wear-and-tear scale for therapeutic footwear, results of a generalizability study
Bibliographic record
Abstract
OBJECTIVE: Therapeutic footwear is often prescribed at considerable cost. Foot-care specialists normally assess the wear-and-tear of therapeutic footwear in order to monitor the adequacy of the prescribed footwear and to gain an indicator of its use. We developed a simple, rapid, easily applicable indicator of wear-and-tear of therapeutic footwear: the wear-and-tear scale. The aim of this study was to investigate the intra- and inter-rater reliability of the wear-and-tear scale. METHODS: A test set of 100 therapeutic shoes was assembled; 24 raters (6 inexperienced and 6 experienced physiatrists, and 6 inexperienced and 6 experienced orthopaedic shoe technicians) rated the degree of wear-and-tear of the shoes on the scale (range 0-100) twice on 1 day with a 4-h interval (short-term) and twice over a 4-week interval (long-term). Generalizability theory was applied for the analysis. RESULTS: Short-term, long-term and overall intra-rater reliability was excellent (coefficients 0.99, 0.99 and 0.98; standard error of measurement (SEM) 2.6, 2.9 and 3.9; smallest detectable changes (SDC) 7.3, 8.0 and 10.8, respectively). Inter-rater reliability between professions, experience and inexperienced raters, and overall was excellent (coefficients 0.97, 0.98 and 0.93; SEM 4.9, 4.5, and 8.1; SDC 13.7, 12.4 and 22.5, respectively). CONCLUSION: The wear-and-tear scale has excellent intra-rater, inter-rater, and overall 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 | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".