Comparing surgical outcomes of anterior capsular release vs circumferential release for persistent capsular stiffness
Bibliographic record
Abstract
Purpose: To consolidate the existing literature evaluating anterior capsular release and circumferential capsular release in the treatment of adhesive capsulitis (AC) of the shoulder. Methods: The electronic databases PUBMED, EMBASE, MEDLINE and CENTRAL (Cochrane Central Register of Controlled Trials) were searched from data inception to October 8, 2020. Data are presented descriptively where appropriate. A meta-analysis was conducted for patient-reported outcomes. Results: Overall, there were forty-six articles included. The majority of patients underwent circumferential release compared to anterior release (80.1% vs. 19.9%). Concomitant Manipulation Under Anesthesia (MUA) was employed in 25 studies, with a higher occurrence in the anterior compared to the circumferential release group (70% vs 60%). Both groups experienced significant improvements postoperatively in range of motion (ROM) and patient-reported outcomes. Complication rates were low for both anterior release (0.67%) and 360° release (0.44%). Conclusion: Both anterior and circumferential release are effective techniques for treating AC with low complication rates. Future studies should improve documentation of patient demographics, surgical techniques and outcomes to determine an individualized treatment protocol for patients. Level of evidence: Level IV, Systematic Review of Level I-IV studies.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".