Differences in orthotic design for thumb osteoarthritis and its impact on functional outcomes
Bibliographic record
Abstract
BACKGROUND: Orthoses are a well-known intervention for the treatment of thumb osteoarthritis; however, there is a multitude of orthotic designs and not enough evidence to support the efficacy of specific models. OBJECTIVE: To examine the influence of different orthoses on pain, hand strength, and hand function of patients with thumb osteoarthritis. STUDY DESIGN: Literature review. METHODS: A scoping literature review of 14 publications reporting orthotic interventions for patients with thumb osteoarthritis was conducted. Functional outcomes and measures were extracted and analyzed. RESULTS: In total, 12 studies reported improvements in pain and hand strength after the use of thumb orthoses. Comparisons between different orthotic designs were inconclusive. CONCLUSION: The use of orthoses can decrease pain and improve hand function of patients with thumb osteoarthritis; however, the effectiveness of different orthoses still needs support through adequate evidence. Clinical relevance Multiple orthoses for thumb osteoarthritis are available. Although current studies support their use to improve pain and hand function, there is no evidence to support the efficacy of specific orthotic designs. Improved functional outcomes can be achieved through the use of short orthoses, providing thumb stabilization without immobilizing adjacent joints.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".