Will my shoulder pain get better? – secondary analysis of data from a multi-arm randomised controlled trial
Bibliographic record
Abstract
OBJECTIVE: To determine whether higher level or improvements over time in pain self-efficacy (PSE) and expectations of intervention effectiveness lead to better outcomes and whether the intervention used to manage rotator cuff related shoulder pain (RCRSP) impacts PSE and expectations over time. DESIGN: Secondary analysis of data from a randomised controlled trial. PARTICIPANTS: 123 individuals (48 [15] years old; 51% female) with RCRSP. INTERVENTIONS: Participants randomised into one of three 12-weeks interventions (education; education and motor control exercises; education and strengthening exercises). MAIN OUTCOME MEASURES: QuickDASH and Western Ontario Rotator Cuff Index (WORC) were administered at baseline and 12 weeks. Pain self-efficacy was assessed at 0 and 6 weeks. Patients' expectations regarding intervention effectiveness were assessed before randomisation and after the first and the last intervention sessions. NparLD were used for the analyses. A time effect indicated a significant change in patients' expectations or PSE over time, while a resolution effect indicated a significant difference in patients' expectations or PSE between those whose symptoms resolved and those whose did not. RESULTS: Patients' expectations (-3 to 3) increased over time (0.33/3 [0.19 to 0.77]). Overall expectations were higher for those who experienced symptom resolution based on the WORC (0.19/3 [0.05 to 0.33]). PSE increased over time (5.5/60 [3.6 to 7.4]). Overall PSE was higher for those who experienced symptom resolution based on the WORC (7.0 [3.9 to 10.1]) and the QuickDASH (4.9 [1.7 to 8.2]). CONCLUSION: Clinicians should consider monitoring PSE and patients' expectations as they are important indicators of outcome. CONTRIBUTION OF THE PAPER.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".