Long-Term Outcomes Following Manipulation Under Anaesthetic for Patients with Primary and Secondary Frozen Shoulder
Bibliographic record
Abstract
BackgroundFrozen Shoulder (FS) is a common, debilitating condition for which manipulation under anaesthetic (MUA) is a non-invasive and effective treatment option. Current literature evaluates short to medium-term outcomes, but there is a paucity of long-term (>10 years) studies. Knowledge of long-term outcomes is also needed to evaluate whether FS or its treatment pre-disposes to other shoulder pathology in the long-term.MethodsA retrospective analysis of 398 shoulders undergoing MUA for FS between Jan 1999 and Jan 2010; 240 complete datasets were obtained. Outcomes were Oxford Shoulder Score (OSS), recurrence and development of other shoulder pathology (arthritis or rotator cuff tear).ResultsAt long-term follow-up (mean 13.2 years), 71.3% had no symptoms (OSS 48), 16.6% had minor symptoms (OSS 42–47) and 12.1% had significant symptoms (OSS < 42). There were 4/240 (1.7%) self-reported recurrences > 5 years after initial MUA and 2/240 (0.8%) repeat MUAs. In the long-term 6.7% developed rotator cuff pathology and 3.8% shoulder OA.DiscussionThis study suggests that long-term outcome after MUA for FS is favourable. Late recurrence of FS is uncommon and the development of OA or rotator cuff pathology is no greater than that of the general population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".