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Record W1984546493 · doi:10.1016/j.pain.2006.06.004

Screening for pain phenotypes: Analysis of three congenic mouse strains on a battery of nine nociceptive assays

2006· article· en· W1984546493 on OpenAlexafffund
Jeffrey S. Mogil, Jennifer Ritchie, Susana G. Sotocinal, Shad B. Smith, Sylvie Croteau, Daniel J. Levitin, Anna K. Naumova

Bibliographic record

VenuePain · 2006
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcGill University Health CentreMcGill University
FundersNational Institute on Drug AbuseCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsCongenicNociceptionQuantitative trait locusGenotypeBiologyGeneticsPhenotypeGene

Abstract

fetched live from OpenAlex

In an attempt to identify new genes responsible for variability in pain sensitivity, we tested three congenic mouse strains--in which a small portion of the genome of the MOLF/Ei strain has been placed on a C57BL/6 genetic background--on a battery of nine nociceptive assays, chosen to reflect those assays in most common use in the pain literature. Mice of both sexes were evaluated by two different testers at different points in time, allowing us to examine the relative importance of genotype, sex, tester and cohort effects on data from these assays. We find strong evidence for the existence of two quantitative trait loci (i.e., genomic regions containing variability-causing genes), one for thermal nociception on mouse chromosome (Chr) 17 (Chr 17; Tpnr3) and one for formalin test nociception on mouse Chr 12 (Nociq3). We note, however, that the nociceptive assays in this battery feature strong main effects and interactions of sex, tester, and cohort, which if not controlled or covaried can seriously confound interpretation of genetic experiments, including the comparison of transgenic knockout mice to their wild-type controls.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.032
GPT teacher head0.279
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 teacher head, 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

Citations80
Published2006
Admission routes2
Has abstractyes

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