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Rethinking the temporal dynamics of pain: from 3 months to 3 hours?

2025· review· en· W4415161079 on OpenAlexafffund
Nader Ghasemlou

Bibliographic record

VenuePain · 2025
Typereview
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaMultiple Sclerosis Society of Canada
KeywordsRhythmPerceptionPrecision medicineCLOCKPain medicineAffect (linguistics)Pain perceptionPersonalized medicine

Abstract

fetched live from OpenAlex

ABSTRACT: The perception of pain varies rhythmically across the day. Although early studies showed variations in pain sensitivity, mechanistic studies are only now identifying how clock genes and individual rhythms affect pain outcomes. Core clock genes, including Bmal1 and Cry1, regulate pain through neuroimmune interactions, providing potential biomarkers to understand individual variability. This rhythmicity offers a promising avenue for a precision medicine approach to identify optimal timing for pain therapies and stratification of individuals. Integrating daily rhythmic patterns with digital health tools could significantly enhance treatment strategies, moving beyond the conventional "one-size-fits-all" approach toward personalized pain management.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.046
GPT teacher head0.300
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes2
Has abstractyes

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