Application of mutagen sensitivity assay in a glioma case-control study
Bibliographic record
Abstract
Few risk factors for glioma have been identified other than ionizing radiation. The alkylating agent acrylamide is a compound found in both occupational and the general environment and identified as one of the forty known or suspected neurocarcinogens in animal models. The mutagen sensitivity assay (MSA) has been used to indirectly show reduced DNA repair capacity upon exposure to ionizing radiation in those with glioma compared to controls. In this study, MSA was used to assess its applicability to a glioma case-control study and to test the hypothesis that subjects with glioma may have lower DNA repair capacity after exposure to selected potential human neurocarcinogens (i.e. acrylamide), compared to controls. Approximately 50 case and 50 control subjects were identified from a clinic-based study that investigated environmental risk factors for glioma, who completed an exposure survey, and had frozen immortalized lymphocytes available. A total of 50 metaphase spreads were read and reported for each participant. The association of case-control status with MSA for acrylamide, i.e. breaks per spread, was examined by multivariable logistic regression models. The mean number of breaks per slide was similar between hospital-based controls and cases. In addition, case-control status or exposure categories were not associated with the number of breaks per spread. Although the MSA has been shown as a useful molecular epidemiology tool for identifying individuals at higher risk for cancer, our data do not support the hypothesis that glioma patients have reduced DNA repair capacity in response to exposure to acrylamide. Further research is needed before the MSA is utilized in large-scale epidemiological investigations of alkylating agents.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".