Research priorities for non-pharmacological therapies for common musculoskeletal problems: nationally and internationally agreed recommendations
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal problems such as low back pain, neck, knee and shoulder pain are leading causes of disability and activity limitation in adults and are most frequently managed within primary care. There is a clear trend towards large, high quality trials testing the effectiveness of common non-pharmacological interventions for these conditions showing, at best, small to moderate benefits. This paper summarises the main lessons learnt from recent trials of the effectiveness of non-pharmacological therapies for common musculoskeletal conditions in primary care and provides agreed research priorities for future clinical trials. METHODS: Consensus development using nominal group techniques through national (UK) and international workshops. During a national Clinical Trials Thinktank workshop in April 2007 in the UK, a group of 30 senior researchers experienced in clinical trials for musculoskeletal conditions and 2 patient representatives debated the possible explanations for the findings of recent high quality trials of non-pharmacological interventions. Using the qualitative method of nominal group technique, these experts developed and ranked a set of priorities for future research, guided by the evidence from recent trials of treatments for common musculoskeletal problems. The recommendations from the national workshop were presented and further ranked at an international symposium (hosted in Canada) in June 2007. RESULTS: 22 recommended research priorities were developed, of which 12 reached consensus as priorities for future research from the UK workshop. The 12 recommendations were reduced to 7 agreed priorities at the international symposium. These were: to increase the focus on implementation (research into practice); to develop national musculoskeletal research networks in which large trials can be sited and smaller trials supported; to use more innovative trial designs such as those based on stepped care and subgrouping for targeted treatment models; to routinely incorporate health economic analysis into future trials; to include more patient-centred outcome measures; to develop a core set of outcomes for new trials of interventions for musculoskeletal problems; and to focus on studies that advance methodological approaches for clinical trials in this field. CONCLUSION: A set of research priorities for future trials of non-pharmacological therapies for common musculoskeletal conditions has been developed and agreed through national (UK) and international consensus processes. These priorities provide useful direction for researchers and research funders alike and impetus for improvement in the quality and methodology of clinical trials in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.463 | 0.428 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.019 | 0.028 |
| Research integrity | 0.032 | 0.042 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".