Management of Chronic Pain in Long-Term Care: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: Pain, a complex subjective experience, is common in care home residents. Despite advances in pain management, optimal pain control remains a challenge. In this updated systematic review, we examined effectiveness of interventions for treating chronic pain in care home residents. DESIGN: A Cochrane-style systematic review and meta-analysis using PRISMA guidelines. SETTING AND PARTICIPANTS: Randomized and nonrandomized controlled trials and intervention studies included care home residents aged ≥60 years receiving interventions to reduce chronic pain. METHODS: Six databases were searched to identify relevant studies. After duplicate removal, articles were screened by title and abstract. Full-text articles were reviewed and included if they implemented a pain management intervention and measured pain with a standardized quantitative pain scale. Meta-analyses calculated standardized mean differences (SMDs) using random-effect models. Risk of bias was assessed using the Cochrane Risk-of-Bias Tool 2.0. RESULTS: We included 42 trials in the meta-analysis and described 13 more studies narratively. Studies included 26 nondrug alternative treatments, 8 education interventions, 7 system modifications, 3 nonanalgesic drug treatments, 2 analgesic treatments, and 9 combined interventions. Pooled results at trial completion revealed that, except for nonanalgesic drugs and health system modification interventions, all interventions were at least moderately effective in reducing pain. Analgesic treatments (SMD -0.80; 95% CI -1.47 to -0.12; P = .02) showed the greatest treatment effect, followed by nondrug alternative treatments (SMD -0.70; 95% CI -0.95 to -0.45; P < .001), combined interventions (SMD -0.37; 95% CI -0.60 to -0.13; P = .002), and education interventions (SMD -0.31; 95% CI -0.48 to -0.15; P < .001). CONCLUSIONS AND IMPLICATIONS: Our findings suggest that analgesic drugs and nondrug alternative pain management strategies are the most effective in reducing pain among care home residents. Clinicians should also consider implementing nondrug alternative therapies in care homes, rather than relying solely on analgesic drug options.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".