What is known and what is still unknown within chronic musculoskeletal pain? A systematic evidence and gap map
Bibliographic record
Abstract
ABSTRACT: Evidence and gap maps (EGMs) can be used to identify gaps within specific research areas and help guide future research agendas and directions. Currently, there are no EGMs within the broad domain of chronic musculoskeletal (MSK) pain in adults. The aim of this study was to create a contemporary EGM of interventions and outcomes used for research investigating chronic MSK pain. This EGM was based on systematic reviews of interventions published in scientific journals within the past 20 years. Embase, PubMed, the Cochrane Library, and PsycINFO were used to retrieve studies for inclusion. The quality of the included reviews was assessed using AMSTAR-II. Interventions were categorised as either physical, psychological, pharmacological, education/advice, interdisciplinary, or others. Outcomes were categorised using the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) recommendations. Of 4299 systematic reviews, 457 were included. Of these, 50% were rated critically low quality, 25% low quality, 10% moderate quality, and 15% rated high quality. Physical interventions (eg, exercise therapy) and education were the most common interventions reported in 80% and 20% of the studies, respectively. Pain (97%) and physical functioning (87%) were the most reported outcomes in the systematic reviews. Few systematic reviews used interdisciplinary interventions (3%) and economic-related outcomes (2%). This contemporary EGM revealed a low proportion of high-quality evidence within chronic MSK pain. This EGM clearly outlines the lack of high-quality research and the need for increased focus on interventions encompassing the entire biopsychosocial perspective.
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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 | 0.159 | 0.380 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.051 | 0.032 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| 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".