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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".