Prognostic factors of nonsurgical intervention outcomes for patients with frozen shoulder: a retrospective study
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the correlation between mental and physical health-related quality of life and the outcomes of conservative treatment in patients with frozen shoulder (FS). METHODS: This was a two-center retrospective study. It included 84 consecutive patients who underwent a 3-month treatment comprising education, physical therapy, and corticosteroid-anesthetic injections. Changes in range of motion (ROM) and Shoulder Pain and Disability Index (SPADI) scores, measured at baseline and after 3 months, were selected as dependent variables. Data on age, sex, Body Mass Index, duration of symptoms, dominant affected limb, and Short Form-36 (SF-36) subscales were gathered at baseline and investigated as prognostic factors. Backward stepwise regression models were used to identify significant associations. RESULTS: At 3-month follow-up, all the patients showed significant improvement. Higher SF-36 General Health, Mental Health and Social Functioning scores at baseline were associated with a greater beneficial change in ROM and SPADI. In contrast, lower SF-36 Bodily Pain and Role Emotional scores were found to be associated with greater improvement. CONCLUSION: The study findings indicate that the self-perceived mental and physical health of patients have a significant impact on both subjective and objective clinical outcomes and healthcare professionals should take these aspects into account. LEVEL OF EVIDENCE: .
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".