Conservative Management of de Quervain Stenosing Tenosynovitis: Review and Presentation of Treatment Algorithm
Bibliographic record
Abstract
BACKGROUND: Nonsurgical management of de Quervain disease relies mainly on the use of oral nonsteroidal antiinflammatory drug administration, splint therapy, and corticosteroid injections. Although the latter is most effective, with documented success rates of 61 to 83 percent, there exists no clear consensus pertaining to conservative treatment protocols conferring the best outcomes. This article reports on all present conservative treatment modalities in use for the management of de Quervain disease and highlights specific treatment- and patient-related factors associated with the best outcomes. METHODS: A systematic search was performed using the PubMed database using appropriate search terms; two independent reviewers evaluated retrieved articles using strict inclusion and exclusion criteria. RESULTS: A total of 66 articles met the inclusion criteria for review, consisting of 22 articles reporting on outcomes following a single conservative treatment modality, eight articles reporting on combined treatment approaches, 13 articles directly comparing different conservative treatment regimens, and 23 case reports. CONCLUSIONS: A multimodal approach using splint therapy and corticosteroid injections appears to be more beneficial than either used in isolation. Although there exists some evidence showing that multipoint injection techniques and multiple injections before surgical referral may provide benefit over a single point injection technique and a single injection before surgery, corticosteroid use is not benign and should thus be performed with caution. Ultrasound was proven valuable in the visualization of an intercompartmental septum, and ultrasound-guided injections were shown to both be more accurate and confer better outcomes. Several prior and concurrent medical conditions may affect conservative treatment outcome. A Level I to II evidence-based treatment protocol is recommended for the optimal nonsurgical management of de Quervain disease.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".