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Record W2898824938 · doi:10.1515/jbcpp-2017-0218

Clinical assessment of arthritic knee pain by infrared thermography

2018· review· en· W2898824938 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.

Bibliographic record

VenueJournal of Basic and Clinical Physiology and Pharmacology · 2018
Typereview
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsDalhousie University
Fundersnot available
KeywordsThermographyRheumatoid arthritisMedicineOsteoarthritisSkin temperatureArthritisPhysical therapyInflammatory arthritisKnee painDermatologyInternal medicineInfraredPathology

Abstract

fetched live from OpenAlex

Background Infrared thermography (IRT) provides accurate measurements of surface temperatures. In inflammatory conditions such as arthritis, tissue temperature is elevated, which can be measured on the periarticular skin surface by IRT. The aim of this review is to evaluate the evidence for the relationship between skin temperature (measured by IRT) and arthritic knee pain and discuss the limitations of IRT in clinical settings of arthritis. Method To reach this goal, a mini-review of all the relevant papers indexed in PubMed was conducted. Results Several studies suggest a significant correlation between skin temperature assessed by IRT and the severity of arthritic knee pain (especially in osteoarthritis and rheumatoid arthritis). Conclusion IRT is a reliable technique to assess inflammatory arthritis 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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.461
Teacher spread0.410 · 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