Relative motion splints versus metacarpophalangeal joint blocking splints in the management of trigger finger: Study protocol for a randomized comparative trial
Bibliographic record
Abstract
BACKGROUND: Evidence supports the use of hand-based metacarpophalangeal joint (MCPJ) blocking splints as an intervention for trigger finger (TF). In practice, finger-based relative motion (RM) splints are also implemented without evidence. PURPOSE: This randomized comparative trial (RCT) aims to evaluate implementation of MCPJ blocking and RM splints for effectiveness, function, occupational performance and wearability after 6 weeks of TF management. METHODS AND ANALYSIS: Priori analysis determined 36 individuals were needed for random assignment to the RM or MCPJ blocking splint groups. Individuals must be aged ≥21 years, and diagnosed with TF involving ≥1 finger. For blinding purposes, the primary author screens for eligibility, fabricates the splints and educates. Therapist A administers the primary outcome measures Week-1 and Week-6-stage of stenosing tenosynovitis and secondary outcome measures- number of triggering events in 10 active fists, visual analog scales (VAS) for pain, splint comfort and satisfaction, Disabilities of the Arm, Shoulder and Hand, and Canadian Occupational Performance Measure. Therapist B in Week-3 instructs participants in deep tissue massage and administers splint wearability VASs. The RM pencil test is used to determine the affected finger(s) MCPJ splint position i.e., more extension or flexion based on participant response. The MCPJ blocking splint holds the MCPJ in a neutral position. Analysis involves a mixed-effects ANOVA to compare Week-1 and Week-6 primary and secondary outcomes. RESULTS: Recruitment and data collection are ongoing. DISCUSSION: Biomechanically RM splints control tendon excursion and reduce passive tendon tension while allowing unencumbered finger motion and hand function. Hence clinicians use RM splints as an intervention for TF, despite the lack of implementation evidence. This RCT implements a function-focused as well as patient-centered approach with partial blinding of assessors and participants. CONCLUSION: We anticipate that this study will provide evidence for the implementation of RM splints to manage adults with TF. TRIAL REGISTRATION: Clinical trial registration This trial is registered with ClinicalTrials.gov (NCT05763017).
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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 | 0.036 | 0.040 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.091 | 0.015 |
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".