Recent Developments in Defining Microinvasive and Early Invasive Carcinoma of the Uterine Cervix
Bibliographic record
Abstract
Although only a small proportion of invasive squamous carcinoma of the cervix present with microinvasive disease, consistent recognition of this entity is important because it carries important management implications. The objective of this review was to reassess the methods and criteria for a histopathologic diagnosis of both early invasive squamous and adenocarcinomas in light of recent pathologic and clinicopathologic studies. The diagnosis of microinvasion is primarily histopathologic. Although the concept of microinvasion initially seems obvious, there are problems in diagnostic precision. A clear understanding of both the Society of Gynaecologic Oncologists' and the International Federation of Obstetricians and Gynecologists' classifications of early invasive disease is required. Subsequently, key parameters must be assessed-measurement of depth and lateral spread, assessment of margins, and identification of lymphovascular invasion-using accepted reference points and definitions. This assessment requires properly oriented and stained histologic sections of a loop electrosurgical excision procedure or cone specimen. Immunohistochemical staining of vascular endothelium or epithelial basement membrane has only a limited/adjunctive role. Controversy continues regarding the need to appraise the extent of any lymphovascular invasion and measurement in cases with multifocal invasion. Application of criteria to invasive adenocarcinomas seems warranted but is particularly challenging because of its special morphology and different biology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".