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Record W2067594929 · doi:10.1016/s0304-3959(01)00477-8

Altered central nervous system processing of noxious stimuli contributes to decreased nociceptive responding in individuals at risk for hypertension

2002· article· en· W2067594929 on OpenAlexfundno aff
Christopher France, Shannon A. Froese, Jesse C. Stewart

Bibliographic record

VenuePain · 2002
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteNational Institutes of HealthMcGill University
KeywordsNociceptionNoxious stimulusNeuroscienceSummationHyperalgesiaMedicineCentral nervous systemThreshold of painPsychologyAnesthesiaInternal medicineStimulationReceptor

Abstract

fetched live from OpenAlex

Previous evidence indicates that individuals with hypertension and those at increased risk for the disorder exhibit decreased pain perception. To test the hypothesis that attenuation of nociceptive processing in individuals at genetic risk for hypertension is related to differential central modulation of nociceptive transmission, the present study examined descending modulation, alpha-motoneuron excitability, and temporal summation of nociceptive input in young adults with and without a parental history of hypertension. Nociceptive flexion (NFR) and non-nociceptive Hoffman reflexes were assessed at rest and during performance of a mental arithmetic task. Temporal summation was assessed by examining NFR threshold in response to a series of five electrical pulses delivered at 2 Hz. Compared to participants without a parental history of hypertension, offspring of individuals with hypertension exhibited significantly higher NFR thresholds, suggesting that risk for hypertension may be associated with enhanced activation of central pain inhibition pathways.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.033
GPT teacher head0.273
Teacher spread0.240 · 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 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

Citations63
Published2002
Admission routes1
Has abstractyes

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