Utility of p16ink4a Immunocytochemistry in Liquid-Based Cytology Specimens from Women Treated for High-Grade Squamous Intraepithelial Lesions
Bibliographic record
Abstract
OBJECTIVE: To examine whether p16(ink4a) immunocytochemical (ICC) expression detected intraepithelial disease in liquid-based cytology (LBC) specimens from women with high-grade squamous intraepithelial lesions (HSIL), whose specimen was labeled negative for intraepithelial lesion or malignany (NILM). STUDY DESIGN: Residual LBC specimens from women treated for HSIL (n = 21), whose LBC test was interpreted as NILM including marked benign inflammatory changes (BCC) were used. The control (n = 25) consisted of residual LBC specimens from women with documented HSIL. ICC for p16p(16k4a) was performed on a second ThinPrep (ThinPrep 2000, Cylyl Corporation, Boxborough, Massachusetts, U.S.A.) preparation; the percentage ofpositive cells and intensity of immunostaining were recorded. Standard LBC preparations for p16(ink4a) ICC-positive and ICC-negative control cases were reviewed. RESULTS: Twenty-four of 25 (96%) of the HSIL control group were ICC p16(ink4a) positive. In the NILM/BCC group, 2 of 21 with adequate LBC residua were ICC p16(ink4a) positive; on review both were reclassified as epithelial abnormality--1 HSIL and 1 atypical squamous cells cannot exclude HSIL. In both, subsequent colposcopic biopsy yielded HSIL. CONCLUSION: p16(ink4a) ICC positivity on NILM/BCC LBC residua from patients with HSIL may identify cases that merit cytologic review and possible reclassification. The utility of p16(ink4a) ICC in this situation requires further study.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".