Squamous Intraepithelial Lesions in Cervical Tissue Samples of Limited Adequacy and Insufficient for Grading as Low or High Grade
Bibliographic record
Abstract
OBJECTIVE: Cervical tissue samples of limited adequacy but with pathological features of squamous intraepithelial lesions (SIL) may not be gradable and result in a diagnosis of ungraded SIL (SILQ). SILQ outcome, clinico-pathological correlates, and the predictive role of biomarker staining are unknown. MATERIALS AND METHODS: Among 17,551 colposcopy attendees, 478 (2.7%) had SILQ. Glass slides of 472 were reviewed. Positive [high SIL (HSIL), adenocarcinoma in situ (AIS), or carcinoma] and negative [negative for intraepithelial lesion or malignancy (NILM) or low SIL (LSIL)] outcomes were based on the worst pathology in 24 months of follow-up. p16 and Ki67 immunohistochemistry of 80 random SILQ and 149 controls (44 NILM, 15 LSIL, 75 HSIL, and 15 AIS) was scored as unsatisfactory, positive, or negative. Biomarker and outcome status were correlated, and sensitivity, specificity positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS: Of the total cases, 332 (1.9%) were reviewed as SILQ, and follow-up for 329 was positive in 134 (41%). Atypical glandular cells, AIS, atypical squamous cells (cannot exclude HSIL), HSIL referral Pap test (70% vs. 47%, p < .001), and HSIL colposcopic impression (33% vs. 19%, p < .001) were more frequent among positive compared with negative outcomes. Best SILQ sensitivity (89%) and NPV (77%) occurred with combined biomarkers, and best specificity (52%) and PPV (58%) occurred with Ki67. All 4 performance metrics among the controls were high. CONCLUSIONS: The 2% frequency and 41% positive outcome highlight the clinical importance of SILQ. The referral Pap test and colposcopic impression could prioritize follow-up colposcopy for some SILQ, and negative staining with both biomarkers could eliminate further colposcopy in others.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".