A retrospective study on the short-term efficacy of injection and manipulation in primary frozen shoulder
Bibliographic record
Abstract
There is no consensus on the optimal treatment approach for frozen shoulder. This study aimed to evaluate the short-term clinical efficacy of intra-articular injections combined with manipulative therapy, intra-articular injections alone, and local steroid pain point injection for the treatment of primary frozen shoulder. A retrospective study was conducted on 202 patients with frozen shoulder who visited the Department of Orthopedics from January 2020 to April 2022. This study followed the Consolidated Standards of Reporting Trials guidelines for reporting retrospective studies. The patients underwent 3 treatment modalities: intra-articular injection combined with manipulation (lidocaine 100 mg, betamethasone 5 mg, and saline 100 mL), intra-articular injection alone (lidocaine 100 mg, betamethasone 5 mg, and saline 100 mL), and local steroid pain point injection (lidocaine 100 mg, betamethasone 5 mg, and saline 5 mL). The patient's pain and functional improvement were assessed using the Simplified McGill Pain Scale and the Constant-Murley score before treatment, and at 6 and 12 months after treatment. A systematic review of the literature on hydrodilatation with corticosteroids for the treatment of frozen shoulders was also conducted. There were no differences in baseline indicators among the 3 treatment groups. At the 6-month and 12-month follow-up visits, the McGill pain scores of the intra-articular injection combined with manipulation group were significantly lower than those of the other groups (P < .001), while the Constant-Murley scores were significantly higher (P < .001). After 12 months of follow-up, the total effective rate was 94.6% for the intra-articular injection combined with manipulation group, 90.9% for the intra-articular injection group, and 87.5% for the local steroid pain point injection group. Intra-articular injection combined with manipulative release can rapidly alleviate pain symptoms and improve joint function in patients with frozen shoulder. This procedure is simple, safe, and suitable for use in medical institutions of all levels.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".