AgNOR technique could be useful for differential diagnosis of squamous carcinomatous cells and atypical cells related to human papillomavirus in cervical smears
Bibliographic record
Abstract
The human papillomavirus (HPV)-associated effect can mimic invasive squamous cell carcinoma. Measurements of the argyrophilic nucleolar organizer region (AgNOR) area of cells from smears with HPV infection of the cervix with marked atypia were carried out to differentiate this pathology from keratinizing carcinomas. After destaining, the smears were incubated in the dark for 25 min with a mixture of silver nitrate and gelatin in formic acid. After washing with deionized water and sodium thiosulfate, the slides were dehydrated and mounted with Canada balsam. The average AgNOR area was determined by image cytometry using the immersion oil objective and selecting 100 cells in each smear. Twenty-three patients with a mean AgNOR area of 1.32 microm(2) among their samples showed normal colposcopies and cervical smears after 18 months. In four patients, whose samples were diagnosed as atypical squamous cells of undetermined significance with viral atypia, the average AgNOR area was 7.70 microm(2); biopsies showed keratinizing squamous carcinomas in three of these cases and moderately differentiated squamous carcinoma in one of them. We propose a cutoff of 2.2 microm(2) for the AgNOR area of cells from smears with HPV infection of the cervix with marked atypia to differentiate this group from keratinizing carcinomas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".