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Record W2320682806 · doi:10.1093/pm/pnv105

Quantitative Sensory Testing in Chronic Musculoskeletal Pain

2016· review· en· W2320682806 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePain Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsSt Joseph's Health CentreMcMaster UniversityHamilton Health Sciences
FundersCanadian Institutes of Health Research
KeywordsQuantitative sensory testingMedicineMusculoskeletal painChronic painSensory systemPhysical therapyPhysical medicine and rehabilitationNeurosciencePsychology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.109
GPT teacher head0.412
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it