Current concepts on the intervention for adhesive capsulitis
Bibliographic record
Abstract
Adhesive capsulitis, or frozen shoulder, is characterized by pain and progressive restriction of both active and passive shoulder range of motion. The pathophysiology involves an initial inflammatory phase with elevated cytokines, followed by pathological fibrosis, capsular thickening, and contracture involving both intra- and extra-articular structures, including the coracohumeral ligament and rotator cuff interval. Diagnosis is primarily clinical. The traditional three-stage model, freezing, frozen, and thawing, has been challenged by recent evidence showing that spontaneous recovery is uncommon and that many patients do not fully regain shoulder function without active treatment. This paradigm change emphasizes the necessity of early and focused interventions to maximize functional recovery. While physiotherapy remains the mainstay of management, interventional procedures have gained prominence for their ability to reduce pain and facilitate rehabilitation. Interventional options include intra-articular corticosteroid injections, hydrodilatation, and suprascapular nerve blocks. This narrative review summarizes current evidence on interventional procedures for adhesive capsulitis, highlighting their mechanisms, techniques, and comparative efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".