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Record W2903863698 · doi:10.3233/ch-189408

Laser Doppler-based measurements of periarticular blood flux can be utilized for assessment of arthritis pain: A hypothesis

2018· article· en· W2903863698 on OpenAlexaff
Noura Motabbakani, Christine Lehmann

Bibliographic record

VenueClinical Hemorheology and Microcirculation · 2018
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLaser Doppler velocimetryMedicineArthritisBlood flowVasodilationPerfusionMicrocirculationDoppler effectInternal medicine

Abstract

fetched live from OpenAlex

Arthritis often presents with serious pain of the affected joints. The pain results mainly from underlying inflammatory processes initiated by the immune response to the degenerative structural changes within the joints and is associated with microvascular vasodilation that increases articular blood flow. Laser Doppler-based methods facilitate reliable quantification of microvascular perfusion. We hypothesize that laser Doppler-based measurements of periarticular blood flux can be utilized for assessment of arthritis pain in patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.150
GPT teacher head0.396
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2018
Admission routes1
Has abstractyes

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