Ovarian carcinoma diagnosis: the clinical impact of 15 years of change
Bibliographic record
Abstract
BACKGROUND: Until recently ovarian carcinoma was considered to be a single disease, and treatment decisions were based solely on grade and pre- and postoperative tumour burden. New insights into molecular features, treatment response, and patient demographics led the scientific community to conclude that ovarian carcinoma histotypes are different disease entities. METHODS: In 2002, the pathology specimens from patients in a clinical trial were reviewed by an experienced gynaecopathologist (pathologist A) for translational research purposes. All cases were typed according to what were then current criteria. The identical cohort was now reassessed by the same expert pathologist and independently reviewed by another gynaecopathologist (pathologist B) applying WHO 2014 diagnostic criteria. Survival analyses were done based on the original as well as the new diagnoses, and historical biomarker study results were recalculated. RESULTS: Upon re-review, pathologist A rendered the same histotype diagnosis in only 54% of cases. In contrast, pathologists A and B independently rendered the same diagnosis in 98% of cases. Histotype was of prognostic significance when 2014 diagnoses were used, but was not prognostic using the original (2002) histotype diagnoses. CONCLUSIONS: Our study demonstrates a marked shift in ovarian carcinoma histotype diagnosis over the past 15 years. The new criteria are associated with a very high degree of interobserver reproducibility, allowing for treatment decisions based on histotype. Finally, biomarkers of putative prognostic significance were revealed to be primarily histotype-specific markers, confirming the critical importance of obtaining up-to-date diagnoses rather than accepting archival histotype data in clinical research.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".