Quantitative Sensory Testing in Chronic Musculoskeletal Pain
Bibliographic record
Abstract
BACKGROUND: In recent years, several published articles have demonstrated that quantitative sensory testing (QST) is useful in the analysis of musculoskeletal pain disorders. Based on the evidence from these studies, it is assumed that QST might be a useful tool in the analysis of the pathogenesis, classification, differential diagnosis, and prognosis of chronic musculoskeletal pain. OBJECTIVES: The objective of this paper is to discuss measurement properties of QST and potentials research and clinical applications in musculoskeletal pain. METHODS: This is a review of the current knowledge base on QST as it relates to musculoskeletal pain disorders. We based our summary on articles retrieved from Ovid MEDLINE (1946 to present) including EMBASE, AMED, and PsycINFO databases to search for all published literature focused on QST and musculoskeletal pain. RESULTS: QST has been shown to be related to neural sensitivity in musculoskeletal pain. QST measurement properties have been evaluated for multiple sensory evaluation modalities and protocols with no clear superior instrument or test protocol. The research evidence is incomplete, but suggests potential clinical benefits for predicting outcomes and subtyping pain. Threshold detection testing is commonly used to quantify sensory loss or gain, in current practice and has shown moderate reliability. Intensity/magnitude rating can be assessed on a wide range of rating scales and may be more useful for pain rating in a clinical context. Threshold detection-based testing and intensity/magnitude rating-based testing can be combined to determine pain threshold in clinical evaluation. CONCLUSIONS: Musculoskeletal pain management may benefit from treatment algorithms that consider mechanism, pain quality, or neurophysiological correlates. Non-invasive QST may be helpful to find sensory array of altered nociceptive process. Due to the diverse etiopathogenetic basis of musculoskeletal pain disorders, a broad range of reliable and valid QST tests may be needed to analyze the various disease entities.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".