Screening for pain phenotypes: Analysis of three congenic mouse strains on a battery of nine nociceptive assays
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".