The changing landscape of gynaecological cancer diagnosis: implications for histopathological practice in the 21st century
Bibliographic record
Abstract
The era of molecular medicine has led to dramatically improved understanding of the genetic events that give rise to different types of cancers. In the case of gynaecological malignancies, this has resulted in distinct shifts in how these tumours are diagnosed in routine surgical pathology practice, with an increased emphasis on accurate subtype diagnosis. This has happened across all sites in the gynaecological tract and for most cell types, but in ways that are site-specific and may appear to be subtle, as in most instances the diagnostic terminology has not changed. For example, the diagnosis of clear cell carcinoma of the ovary is still in use, but the diagnostic criteria and clinical implications are different in 2017 from what they were in 2000. As a result, there can be a failure to appreciate how important these changes are and the resulting necessity of incorporating them into our daily practice. In this review we will describe changes in diagnostic surgical pathology occasioned by improved understanding of molecular events during pathogenesis, for cancers of ovary/tube, endometrium, cervix and vulva, and highlight how current practice differs from that of only a few years ago.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".