Dorsal Approach for Corticosteroid Injection in Trigger Finger Management: A Scoping Review
Bibliographic record
Abstract
Introduction: Stenosing tenosynovitis (trigger finger) is a common condition caused by inflammation and hypertrophy of flexor tendons. Corticosteroid injection (CSI) is an effective and safe treatment option. A palmar approach for CSI is typically used; however dorsal injection, which may be less painful, is not well-studied. Methods: A 6-stage scoping review was conducted to characterize outcomes associated with dorsal CSI for trigger finger management. We searched Ovid MEDLINE, EMBASE, and Web of Science for eligible articles in English from inception to July 2024. Data regarding study characteristics and CSI outcomes were synthesized. Risk of bias was assessed using Joanna Briggs Institute Critical Appraisal Tools. Results: Four articles were included in the review, comprising 1 case series, 2 cohort studies, and 1 randomized controlled trial (RCT). Symptom resolution rates following dorsal CSI ranged from 54% to 73.5%, comparable to a palmar approach. Two studies compared dorsal and palmar injections and found no significant differences in effectiveness. Pain scores for dorsal injections were similar or significantly lower than those for palmar injections in 2 studies. No adverse effects or complications were reported with either injection technique. Conclusion: Current evidence suggests a dorsal approach for CSI in trigger finger management is noninferior to a palmar approach in terms of efficacy and safety, with potential benefits in reducing injection-associated pain. However, more high-quality studies, including RCTs, are needed. Future research should assess anesthetic distribution and patient-reported outcomes to better understand the clinical implications of a dorsal CSI approach.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".