Routine Use of Adjunctive p16 Immunohistochemistry Improves Diagnostic Agreement of Cervical Biopsy Interpretation
Bibliographic record
Abstract
The diagnosis of squamous intraepithelial lesions in cervical tissue specimens is subject to substantial variability. Adjunctive immunohistochemical (IHC) staining for p16 has been shown to add objective biomarker information to morphologic interpretation of hematoxylin and eosin (H&E)-stained tissues. In the CERvical Tissue AdjunctIve aNalysis (CERTAIN) study, we systematically analyzed the impact of adjunctive p16 IHC on the accuracy (agreement with reference pathology results) of diagnosing cervical intraepithelial neoplasia of grade 2 or worse (CIN2+) in the United States. Eleven hundred cervical biopsies were divided into 4 sets of 275 cases by stratified randomization. All H&E slides from each set were interpreted by 17 to 18 individual surgical pathologists, for a total of 19,250 reads by 70 surgical pathologists. After a wash-out period and blinding to original results, cases were re-read by the same pathologists using H&E+p16-stained slides. Using expert consensus diagnoses on H&E+p16 as reference, adjunctive p16 IHC use significantly improved diagnostic agreement of surgical pathologists by 4.7% (95% confidence interval [CI], 3.9, 5.4; P<0.0001). This improvement was driven by an increase of 11.5% (95% CI, 9.3, 13.5; P<0.0001) in sensitivity and an increase of 3.0% (95% CI, 2.2, 3.7; P<0.0001) in specificity. Diagnostic performance was significantly increased as well when expert consensus diagnoses established on H&E only was used as reference. Furthermore, interobserver reliability improved significantly from moderate (H&E: κ=0.58) to substantial (H&E+p16: κ=0.73; P<0.0001). Adjunctive use of p16 IHC provides more accurate and reproducible diagnostic results in the interpretation of cervical biopsies, ensuring that more patients are treated correctly without treating more patients.
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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 | 0.033 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".