Usability testing of the COMET (Clinical Outcome Measures Electronic Toolkit) mobile app for outcome measure data collection in individuals with lower limb amputation and clinicians
Bibliographic record
Abstract
Evidence-based clinical care and outcomes research are essential for developing objectively based standards of practice in prosthetic and orthotic care. Traditionally, outcome measures have been collected using paper-based methods. However, digital collection of these measures may offer several advantages. The COMET© (Clinical Outcome Measures Electronic Toolkit) mobile app was developed to facilitate the assessment of commonly used patient-reported and performance-based outcome measures in prosthetics and orthotics. The usability of the COMET app was evaluated among 15 individuals with lower limb amputation and nine clinicians using the System Usability Scale (SUS). The mean SUS score among individuals with lower limb amputation was 84 ± 12 (Grade: A; Percentile: 90-95; Adjective: Excellent; Acceptable), and the mean SUS score among the clinicians was 80 ± 14 (Grade: A-; Percentile: 85-89; Adjective: Good; Acceptable). There were no significant differences in SUS score between the two groups, indicating that the app was acceptable to both groups of participants in clinical settings.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".