Consideration Of Chronic Pain In Trials To Promote Physical Activity For Diabetes: A Systematic Review Of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Chronic pain has been estimated to affect 60% of patients with diabetes and is strongly associated with reduced activity tolerance. We systematically reviewed randomized controlled trials (RCTs) that explored interventions to improve physical activity among patients with diabetes to establish whether co-morbid chronic pain was captured at baseline or explored as an effect modifier and if trials reported a component designed to target chronic pain. METHODOLOGY/PRINCIPAL FINDINGS: We searched CINAHL, Cochrane Central Registry of Controlled Trials, EMBASE, ERIC, MEDLINE, SPORTDiscus and PsycInfo from inception of each database to March 2012 for RCTs that enrolled patients with diabetes and randomly assigned them to an intervention designed to promote physical activity. Two reviewers independently selected trials and abstracted data. We identified 136 trials meeting our inclusion criteria, only one of which that reported capturing chronic pain measures at baseline. No trial reported on specific interventions to address chronic pain as a competing demand, or as an effect modifier. CONCLUSION/SIGNIFICANCE: Only 1 trial identified that aimed to promote physical activity among patients with diabetes reported that co-morbid chronic pain was captured at baseline. No trials reported exploring chronic pain as an effect modifier or targeting it as part of its intervention.
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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 | 0.116 | 0.312 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.015 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".