Features and methods to discriminate between mechanism-based categories of pain experienced in the musculoskeletal system: a Delphi expert consensus study
Bibliographic record
Abstract
ABSTRACT: Classification of musculoskeletal pain based on underlying pain mechanisms (nociceptive, neuropathic, and nociplastic pain) is challenging. In the absence of a gold standard, verification of features that could aid in discrimination between these mechanisms in clinical practice and research depends on expert consensus. This Delphi expert consensus study aimed to: (1) identify features and assessment findings that are unique to a pain mechanism category or shared between no more than 2 categories and (2) develop a ranked list of candidate features that could potentially discriminate between pain mechanisms. A group of international experts were recruited based on their expertise in the field of pain. The Delphi process involved 2 rounds: round 1 assessed expert opinion on features that are unique to a pain mechanism category or shared between 2 (based on a 40% agreement threshold); and round 2 reviewed features that failed to reach consensus, evaluated additional features, and considered wording changes. Forty-nine international experts representing a wide range of disciplines participated. Consensus was reached for 196 of 292 features presented to the panel (clinical examination-134 features, quantitative sensory testing-34, imaging and diagnostic testing-14, and pain-type questionnaires-14). From the 196 features, consensus was reached for 76 features as unique to nociceptive (17), neuropathic (37), or nociplastic (22) pain mechanisms and 120 features as shared between pairs of pain mechanism categories (78 for neuropathic and nociplastic pain). This consensus study generated a list of potential candidate features that are likely to aid in discrimination between types of musculoskeletal pain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".