The association between number of shoulder diagnoses and positive clinical tests with self-reported function and pain: A cross-sectional study of patients with hypermobile joints and shoulder complaints
Bibliographic record
Abstract
BACKGROUND: Patients with hypermobility spectrum disorder (HSD) and shoulder complaints may suffer from symptoms related to shoulder instability, laxity, and hypermobility. It is currently unknown whether having a more complex clinical status with several diagnoses (i.e., anterior instability (AI), multidirectional instability (MDI), and/or symptomatic localised shoulder hypermobility (LSH), relates to higher functional impairments and pain. OBJECTIVES: To investigate the associations between either ≤1, 2, or 3 clinical shoulder diagnoses (AI, MDI, and LSH) or the number (0-10) of positive clinical shoulder tests with shoulder function using the western Ontario shoulder instability index (WOSI, 0-2100, 0 = best) and pain intensity using numerical pain rating scale (NPRS, 0-10, 10 = worse). DESIGN: Exploratory cross-sectional study. METHOD: From a randomised controlled trial, baseline data from 100 participants with HSD and shoulder complaints for at least three months were included. Associations were investigated using linear regression models, adjusted for age, sex, body mass index, and hand dominance. RESULTS: Compared with having ≤1 diagnosis, neither participants with two (WOSI 76.9, 95% CI -136.3, 290.0; NPRS 0.3, 95% CI -0.9, 1.5) nor three (WOSI 35.5, 95% CI -178.5, 249.6; NPRS 0.1, 95% CI -1.1, 1.3) clinical shoulder diagnoses had significantly worse shoulder function or pain. Likewise, the number of positive clinical shoulder tests was not associated with function (WOSI -20.8 95%CI (-55.3, 13.7)) or pain (NPRS -0.1 95%CI (-0.2, 0.1)). CONCLUSIONS: In participants with HSD and shoulder complaints, having more additional shoulder diagnoses or increased number of positive shoulder tests were not related to functional impairments or pain intensities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".