p53 polymorphism in codon 72 and risk of human papillomavirus-induced cervical cancer: effect of inter-laboratory variation
Bibliographic record
Abstract
An association between codon-72 p53 polymorphism and risk of human papillomavirus (HPV)-induced cervical cancer has been found recently, but it has been difficult to replicate. In this study, we assess the impact of inter-laboratory variation in p53 genotyping on the validity of the proposed association. DNA specimens were randomly selected from 54 invasive, squamous cell carcinoma cases, 52 HPV-negative, and 39 HPV-positive controls from a previous case-control study in Brazil. Codon-72 polymorphism was blindly analyzed in three different laboratories. We calculated age- and race-adjusted odds ratios (OR) and 95% confidence intervals (CI) using logistic regression for gauging the association between p53 polymorphism and cervical cancer risk. The proportions of the Arg/Arg, Arg/Pro, and Pro/Pro genotypes varied substantially among laboratories with Kappa coefficients in the 0.49-0.63 range. When disagreement between labs was allowed, the OR for the Arg/Arg genotype, compared to other forms, was as low as 1.5 (95% CI: 0.5-3. 9). In contrast, the OR increased to 8.0 (95% CI: 2.3-28.5) after exclusion of discordant genotypes. Restricting the comparison to HPV-positive controls increased the magnitude of the relation appreciably. After exclusion of all discordant diagnoses, the OR was 21.5 (95% CI: 3.4-137.8), whereas with disagreed genotypes the association was not significant (OR = 2.9, 95% CI: 0.7-11.9). Homozygous codon-72 p53-Arg apparently confers a higher susceptibility to HPV-associated cervical tumorigenesis. However, exposure misclassification consequent to inter-laboratory variation in protocols may affect the ability to detect the association.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".