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Record W2887228563 · doi:10.1080/24740527.2018.1504283

Identification of complex regional pain syndrome in the upper limb: Skin temperature asymmetry after cold pressor test

2018· article· en· W2887228563 on OpenAlexafffund
Tara Packham, Joy C. MacDermid, James R. Bain, Norman Buckley

Bibliographic record

VenueCanadian Journal of Pain · 2018
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsWestern UniversityHamilton Health Sciences
FundersCanadian Institutes of Health Research
KeywordsComplex regional pain syndromeCold pressor testMedicineUpper limbAnesthesiaAnalysis of variancePhysical therapyPhysical medicine and rehabilitationInternal medicineBlood pressureHeart rate

Abstract

fetched live from OpenAlex

BACKGROUND: Skin temperature asymmetry (SkTA) may assist in early identification of complex regional pain syndrome (CRPS), but previous work has been limited by methodological shortcomings including failure to account for the cutaneous nerve distribution where temperature is measured and reliance on laboratory equipment not clinically available. Pilot work suggested that a cold pressor test (CPT) provided a consistent thermoregulatory stress and might increase sensitivity/specificity of SkTA measurements generated reliably by handheld infrared (IR) thermometers. AIMS: This study investigated the sensitivity, specificity, and validity of SkTA in the upper limb to identify CRPS. METHODS: This study was part of a larger clinical trial (the SARA study: www.clinicaltrials.gov NCT02070367). Using IR thermometers, we evaluated SkTA over major peripheral nerve distributions in the hands before and after immersing a single foot in 5°C water for 30 s. Participant groups included healthy volunteers, CRPS, known nerve injury, and hand fracture. RESULTS: < 0.001) explaining 94% of the variance. Post CPT, sensitivity for a >1.5°C SkTA improved to 82.4% from 58.8%, whereas specificity dropped from 56.3% to 43.8%. CONCLUSION: This study adds further support for the accuracy of SkTA as a diagnostic indicator of CRPS. Further precision in estimates will be gained from larger studies, which should also seek to replicate our findings for SkTA in the lower limbs.

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 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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes2
Has abstractyes

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