The Efficacy of Using the Tissue Fragments Present in Cervical Scrapes for the Histologic Diagnosis of Cervical Neoplasia
Bibliographic record
Abstract
Cervical scrapes to diagnose cervical neoplasia, collected by the clinician with brushes, are sent to the Leiden Cytology and Pathology Laboratory (LCPL) in vials containing BoonFix, a noncross-linking coagulant fixative. Because the residual material left in the vials contains tissue fragments with important diagnostic information, we stored the residual material in our archives. The tissue fragments can be mummified and archived in commercially available histology cassettes. We can produce paraffin sections thereof. Immunostaining is beautiful on serial paraffin sections cut from these blocks. We experienced that it is important to leave the brush in the vial such that all tissue fragments can be used for histologic diagnosis. In order to optimize the system, tissue fragments left in the endocervical part of the brush are removed in a paint shaker. We illustrate this principle of recovering mummified tissue fragments in a false negative case with cytology containing many undiagnosable collapsed tissue fragments. This case shows clearly the efficacy of using the tissue fragments present in cervical scrapes for the histologic diagnosis of cervical neoplasia.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.001 |
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".