Development of a low resource exercise rehabilitation application for musculoskeletal disorders to help underserved patients in a primary care setting.
Bibliographic record
Abstract
Objective: We set out to create a Family Medicine EHR (electronic health record) embedded exercise application. This was done to evaluate the utility of the exercise app for providers and to understand the usefulness of the exercise app from the perspective of patients. Methods: This exercise application was developed through an iterative process with repeated pre-testing and feedback from an interprofessional team and embedded into the EHR at an academic family medicine clinic. Anecdotal feedback from patients was used to inform pre-testing adaptations. Results: The application required six iterations prior to clinical utility. It had several features that clinicians and patients felt were beneficial. These features involved a customizable exercise directory with pre-made templated plans which could be further modified. To overcome accessibility barriers, the application was developed to include digital and printable copies with an integrated direct email option for ease of remote sharing with patients. Conclusion: A customizable, open-source exercise application was developed to facilitate provider exercise prescription and support patient self-management. This project may be useful for other providers interested in developing similar programs to address musculoskeletal conditions in their patients. Next steps are to undertake pilot testing of the app with broader provider and patient feedback.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.009 | 0.002 |
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".