Predicting outcomes across treatment settings in patients with shoulder pain referred to physiotherapy: a secondary analysis of two comparable prospective cohort studies
Bibliographic record
Abstract
Objective: Previous studies have examined factors that may contribute to predicting outcomes for patients with shoulder pain. However, there is still a lack of consensus on which factors predict the results and whether there are differences based on the treatment setting. Thus, this study aimed to analyse and compare how baseline variables are associated with future outcomes in patients with shoulder pain in primary and secondary care settings. Methods: This study conducted a secondary analysis of two observational prospective cohort studies involving patients with shoulder pain in primary care (n=150) and secondary care (n=183). Multiple regression analyses were employed, with one interaction term at a time, to examine potential differences in association with baseline characteristics and future outcomes between the two settings. Results: Changes in pain and function were statistically significant at 6 months for patients in primary care and secondary care. However, associations for most baseline variables and outcomes did not differ significantly across these two treatment settings. The only statistically significant interactions observed were for the associations between baseline level of pain, function and fear avoidance beliefs and change in pain scores at 6 months, with lower change scores observed among patients in the secondary care. Conclusion: This study revealed that the association with outcomes did not differ across settings for most baseline characteristics. These findings suggest that it could be feasible to generalise the prognostic value of most baseline variables for patients with shoulder, irrespective of the treatment setting.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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 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".