A Scoping Review of Joint Protection Programs for People with Hand Arthritis
Bibliographic record
Abstract
Background: Joint Protection (JP) can be enhanced by incorporating recent evidence and innovations in collaboration with people with hand arthritis to be salient, useful and effectively implemented. Objective: The purpose of this study is to map the current research on JP principles and guide future research on JP programs for the management of hand arthritis. Methods: A search was performed in 4 databases (PubMed, EMBASE, Google SCHOLAR, CINHAL) from January 1990 to February 2017. A Grey literature was also conducted through the Google web search engine. A combination of search terms was used such as hand osteoarthritis, rheumatoid arthritis, joint protection and/or self-management strategies. Results: Our search found 8,788 citations in which 231 articles were deemed relevant and after duplication 111 articles were retrieved for a full-text review. In total, 40 articles were eligible for data extraction. The majority of the articles were randomized controlled trials (RCTs), systematic reviews and overviews of reviews that investigated joint protection for hand arthritis. Joint protection was tested mostly in rheumatoid arthritis (RA) population and to a lesser extent on hand osteoarthritis and was provided mainly by an occupational therapist. Conclusion: This review synthesized and critically examined the scope of JP for the management of hand arthritis and found that RCTs, systematic reviews and overviews of reviews constituted two-thirds of the current body of literature. Furthermore, it identified a lack of clarity regarding the specific elements of joint protection programs used in clinical studies.
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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 | 0.022 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".