The CERTAIN Study Results
Bibliographic record
Abstract
The Lower Anogenital Squamous Terminology (LAST) Project recommends the use of p16 immunohistochemistry as an adjunct to morphologic assessment of cervical biopsies according to a specific set of criteria. We analyzed the effect of adjunctive p16 according to LAST criteria in a US-based diagnostic utility study involving 70 surgical pathologists providing a total of 38,500 reads on cervical biopsies. Compared with the results obtained using hematoxylin and eosin-stained slides only, including p16-stained slides per LAST criteria increased sensitivity and specificity for diagnosing histologic high-grade squamous intraepithelial lesions across all cases by 8.1% (95% confidence interval [95% CI], 6.5-9.7; P<0.0001) and 3.5% (95% CI, 2.8-4.2; P<0.0001), respectively, using expert consensus diagnoses on hematoxylin and eosin+p16 as reference. Within the subset of cases classified by the pathologists as fulfilling the LAST criteria, adding p16 significantly increased both sensitivity (+11.8%; 95% CI, 9.5-14.0; P<0.0001) and specificity (+9.7%; 95% CI, 7.8-11.5; P<0.0001). However, a comparable improvement in sensitivity (+11.0%; 95% CI, 7.8-14.1; P<0.0001) was found when p16 was used in cases for which p16 staining was not ordered per LAST by the pathologists, whereas specificity decreased by -0.8% (95% CI, -1.1 to -0.5; P<0.0001). The study demonstrates a clinically and statistically significant increase in sensitivity and specificity for high-grade squamous intraepithelial lesion when p16 is used according to LAST criteria. Expanding the use of p16 into non-LAST cases would lead to a comparable improvement in sensitivity within this subgroup of biopsies, at the cost of a minimal, but statistically significant difference in specificity.
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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 | 0.014 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.144 | 0.031 |
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".