Patient-led use of patient-reported outcome measure in self-Management of a Rotator Cuff Injury
Bibliographic record
Abstract
INTRODUCTION: The patient is the person who experiences both the processes and the outcomes of care. Information held by the patient is vital for clinical and self-management, improving health outcomes, delivery of care, organization of health systems, and formulation of health policies. Patient-reported outcome measures (PROMs) play an important role in supporting patient's self-management. This narrative describes a patient-led use of a PROM to self-manage after a rotator cuff injury. METHODS: This is a narrative of a patient who tore the supraspinatus tendon in her right shoulder in an accident. The Disabilities of the Arm, Shoulder and Hand, the DASH questionnaire, was used to monitor and self-manage recovery after the accident. The DASH questionnaire is a self-reported questionnaire that measures the difficulty in performing upper extremity activities and pain in the arm, shoulder or hand. It has been widely used in research studies, but here the patient initiated its use for self-management while waiting for and after rotator cuff surgery. The patient created separate sub-scale scores for function and for pain to answer questions from healthcare providers about her recovery. RESULTS: There was noticeable improvement over 3 months of conservative treatment, from a high level of disability of 56 to 39 (score changed 17); however, the scores were nowhere near the general population normative score of 10.1. Surgery improved the score from 39 pre-surgery to 28. Post-surgical interventions included physiotherapy, pain management and platelet-riched plasma treatment (PRP). The score was 14 4 weeks post-PRP. CONCLUSIONS: The patient found the DASH useful in monitoring recovery from a rotator cuff injury (before and after surgery). The DASH contributed to communication with healthcare professionals and supported the clinical management. The DASH questionnaire was able to capture the patient's experience with the injury and surgical recovery, corroborating an improvement in function while there was persistent post-surgical pain.
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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 | 0.009 | 0.025 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".