Activation of the ERK/MAP Kinase Pathway in Cervical Intraepithelial Neoplasia Is Related to Grade of the Lesion but not to High-Risk Human Papillomavirus, Virus Clearance, or Prognosis in Cervical Cancer
Bibliographic record
Abstract
We subjected 302 archival samples (150 squamous cell carcinomas [SCCs] and 152 cervical intraepithelial neoplasia [CIN] lesions) to immunohistochemical staining with extracellular signal-regulated kinase-1 (ERK1) antibody and human papillomavirus (HPV) testing with 3 primer sets. Follow-up data were available for all SCC cases and 67 CIN cases. High-risk (HR) HPV types were associated with CIN (odds ratio [OR], 19.12; 95% confidence interval [CI], 2.31-157.81) and SCC (OR, 27.25; 95% CI, 3.28226.09). There was a significant linear relationship between lesion grade and ERK1 staining intensity (P = .0001). ERK1 staining was a 100% specific indicator of CIN, with a 100% positive predictive value, but a poor predictor of HR HPV. ERK1 expression did not predict clearance or persistence of HR HPV after CIN treatment. ERK1 staining did not significantly predict survival in cervical cancer in univariate (P = .915) or multivariate analysis. After adjustment for HR HPV, stage, age, and tumor grade in the Cox regression model, only stage (P = .0001) and age (P = .002) remained independent prognostic factors. ERK1 expression seems to be an early marker of cervical carcinogenesis. ERK1 overexpression is not a specific marker of HR-HPV in CIN and cervical cancer, nor does it predict virus clearance after CIN treatment or disease outcome in cervical cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".