Investigation of Laser Cervical Cone Biopsies Negative for Premalignancy or Malignancy
Bibliographic record
Abstract
OBJECTIVE: To measure the rate of carbon dioxide, laser cone biopsies negative for premalignancy or malignancy and determine whether the clinical indications were appropriate or the pathology evaluations were correct. MATERIALS AND METHODS: The patient charts of 95 negative cone biopsies were reviewed by one of the authors to determine the indications for the procedure. All of the slide reviews were done by two of the authors. Following a review of the cone biopsy slides, three deeper sections of the tissue blocks were examined in specimens that were still negative or equivocal for premalignancy. Thereafter, for those still negative the preconization, referral Pap tests, and colposcopic directed tissue samples were reviewed. RESULTS: The overall negative rate of laser cone biopsy was 28% (95/341) and 68% (65/95) were done to investigate high-grade squamous intraepithelial lesions (HGSIL) (cervical intraepithelial neoplasia [CIN] 2,3). There were 25 false negative cone biopsy specimens because of misinterpretation of the original slides or discovery of pathology in additional sections. False positive reporting of some preconization Pap tests or tissue specimens as premalignant when none were seen on review likely resulted in 11 unnecessary conizations. The number of negative cones would thereby be reduced by 36 for a rate of 17% (59/341). CONCLUSIONS: The negative rate could be reduced by 11% with routine deeper sectioning of the tissue blocks of the cone biopsy specimen and improved accuracy of pathological interpretation.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".