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Record W7082987067 · doi:10.17632/v6jc5wwwkv.1

Manuela Lefort-Holguin_2025

2025· dataset· en· W7082987067 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

VenueMendeley Data · 2025
Typedataset
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsQuantitative sensory testingSomatosensory systemNociceptionChronic painOsteoarthritisSensory systemSensitizationHuman studies

Abstract

fetched live from OpenAlex

Chronic osteoarthritis (OA) pain is a complex nociplastic condition that affects humans, as well as cats and dogs. This review summarizes the physiology of pain in healthy individuals, the physiopathology of OA pain, and the use of quantitative sensory testing (QST) to objectively assess somatosensory sensitization associated with chronic OA pain. It discusses the translation of human OA pain phenotype profiles to animals, the management of neuro-sensitization with currently prescribed treatments, and complementary methods for evaluating neuro-sensitization, such as electrodiagnostic testing. Additionally, this review serves as a practical guide for standardizing QST in rats, cats, and dogs, with explanatory appendices. It was hypothesised that in translation with the human condition, OA-induced rat models and naturally occurring OA in cats and dogs would exhibit similar somatosensory sensitization profiles. As observed in human OA, imbalance between facilitatory and inhibitory endogenous controls is present in animal OA, is traced with QST and governs different nociceptive phenotypes. Confirming and validating OA pain profiles will promote a patient-tailored approach to effectively alleviate neuro-sensitization in humans and animals.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0130.011
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 head0.040
GPT teacher head0.289
Teacher spread0.250 · 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