Systematic Review and Synthesis of Mechanism-based Classification Systems for Pain Experienced in the Musculoskeletal System
Bibliographic record
Abstract
OBJECTIVES: Improvements in pain management might be achieved by matching treatment to underlying mechanisms for pain persistence. Many authors argue for a mechanism-based classification of pain, but the field is challenged by the wide variation in the proposed terminology, definitions, and typical characteristics. This study aimed to (1) systematically review mechanism-based classifications of pain experienced in the musculoskeletal system; (2) synthesize and thematically analyze classifications, using the International Association for the Study of Pain categories of nociceptive, neuropathic, and nociplastic as an initial foundation; and (3) identify convergence and divergence between categories, terminology, and descriptions of each mechanism-based pain classification. MATERIALS AND METHODS: Databases were searched for papers that discussed a mechanism-based classification of pain experienced in the musculoskeletal system. Terminology, definitions, underlying neurobiology/pathophysiology, aggravating/easing factors/response to treatment, and pain characteristics were extracted and synthesized on the basis of thematic analysis. RESULTS: From 224 papers, 174 terms referred to pain mechanisms categories. Data synthesis agreed with the broad classification on the basis of ongoing nociceptive input, neuropathic mechanisms, and nociplastic mechanisms (eg, central sensitization). "Mixed," "other," and the disputed categories of "sympathetic" and "psychogenic" pain were also identified. Thematic analysis revealed convergence and divergence of opinion on the definitions, underlying neurobiology, and characteristics. DISCUSSION: Some pain categories were defined consistently, and despite the extensive efforts to develop global consensus on pain definitions, disagreement still exists on how each could be defined, subdivided, and their characteristic features that could aid differentiation. These data form a foundation for reaching consensus on classification.
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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 | 0.059 | 0.198 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.042 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".