The Risk of False-Positive Histology According to the Reason for Colposcopy Referral in Cervical Cancer Screening
Bibliographic record
Abstract
All cervical intraepithelial neoplasia (CIN) diagnoses identified during the New Technologies for Cervical Cancer trial (ISRCTN81678807) were blindly reviewed by 2 pathologists. Original diagnoses based on colposcopy-guided biopsies were compared with those made by the reviewers who had access to all clinical histologic samples (including postsurgical). Cases downgraded from CIN 2+ by the reviewers were considered indicative of unnecessary treatments. The analyses are presented according to the molecular (high-risk human papillomavirus [HPV]) and/or cytologic diagnosis used to refer the women for colposcopy. We reviewed 812 CIN 1 and 364 CIN 2 + diagnoses. The specificity of colposcopy-guided biopsy was 98% and the sensitivity, 84%. The probability of unnecessary treatment was 27% for women with atypical squamous cells of undetermined significance cytologic findings and 8% for women with low-grade squamous intraepithelial lesion or worse, 10% for HPV+ and positive cytologic findings, and 16% for HPV+ alone. The positive predictive value of the first-level screening test was inversely associated with probability of a histologic false-positive result (P = .015). In screening, a low positive predictive value of the colposcopy-referring test may result in unnecessary treatments.
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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 | 0.021 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".