Do brain activity patterns differ between chronic musculoskeletal pain patients and healthy controls as measured by functional neuroimaging techniques? A systematic review protocol
Bibliographic record
Abstract
Background: Chronic musculoskeletal pain has been associated with changes at various levels regarding pain processing. One of the fields that has highlighted such differences between healthy individuals and people suffering from chronic musculoskeletal pain is neuroimaging of the brain. The mixed results arising from the different pathologies in addition to the use of different imaging modalities do not offer a conclusive overview, implicating the interpretation of the findings and their relevance as a causal or a consequent factor to chronification of pain. The aim of this systematic review is to provide a comprehensive summary of the available evidence assessing chronic painful conditions against healthy populations per functional neuroimaging modality. Methods: The protocol for this systematic review was informed and reported in line with the Preferred Reporting Items for Systematic Reviews. The databases that will be searched from inception to a prespecified date include PubMed, EMBASE (Ovid Interface), Scopus, Cochrane Library and Web of Science (Clarivate Analytics). Risk of bias will be assessed with the Newcastle-Ottawa tool and the quality of the cumulative evidence with the Grading of Recommendations, Assessment, Development and Evaluation guidelines. Conclusions: The results of this review will provide an up-to-date report on what the main findings of brain neuroimaging per pathological population and imaging modality are, assessing at the same time the quality of the evidence and indicating future directions in the field of neuroimaging assessment of the musculoskeletal patient. Trial Registration: Current control trial is CRD42024494813.
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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 | 0.060 | 0.072 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.019 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.064 | 0.010 |
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".