The Association of p16INK4A and Fragile Histidine Triad Gene Expression and Cervical Lesions
Bibliographic record
Abstract
OBJECTIVE: This cross-sectional study was intended to assess the association between immunohistochemical analysis of p16 and fragile histidine triad (FHIT) and the presence of precancerous cervical lesions. MATERIALS AND METHODS: Women seen at Pérola Byington Hospital, São Paulo, Brazil, with histologically confirmed cervicitis (n = 31), cervical intraepithelial neoplasia (CIN) 1 (n = 30), CIN 2,3 (n = 30), and cervical cancer (n = 7) had also cervical material collected for liquid-based cytology, human papillomavirus Hybrid Capture 2 (HC2) test, and p16 and FHIT immunohistochemical reactions. RESULTS: p16 and FHIT reactions were scored as the following: <1%, 1% to 5%, >5% to 25%, and >25%. Receiver operating curve analysis was used to select p16 and FHIT score cutoffs for further categorical analyses. All but one of the 37 CIN 2,3/cancer cases had a p16 score of greater than 1% to 5%. Among the 61 cervicitis/CIN 1 cases, 46 (75%) had a p16 score lower than 1% to 5%. In contrast, no association of FHIT expression and severity of cervical lesions could be demonstrated in this data set. Receiver operating curve analyses suggested the score of 1% to 5% for p16 as the cutoff that best discriminates CIN 2,3/cancer from cervicitis/CIN 1. No cutoff for FHIT scores could be suggested with data set. CONCLUSIONS: p16, but not FHIT expression, has the potential to be used as complementary diagnostic tool to investigate human papillomavirus-induced cervical lesions, if these results are confirmed in larger studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".