Adopting a Uniform Approach to Site Assignment in Tubo-Ovarian High-Grade Serous Carcinoma
Bibliographic record
Abstract
There is currently sufficient evidence that nonuterine high-grade serous carcinoma (HGSC) originates in the fallopian tube in the majority of cases, but this is not uniformly reflected in our diagnostic terminology. This is because there remains wide variation in awareness and acceptance of this evidence, which conflicts with traditional views on origin. Accurate disease classification is fundamental to routine clinical practice and research, particularly at a time when exciting new approaches to therapy, early detection, and prevention are appearing on the horizon. We feel the time has come to minimize individual and institutional variations in practice, and agree on an evidence-based approach to uniform terminology and primary site assignment. In this paper we put forward a proposal for a unified approach based on published research evidence and discuss the reasons why it is vital to agree on a uniform protocol. We propose the term "Tubo-ovarian HGSC" in preference to "pelvic" or "Müllerian," as it accurately reflects the origin of this disease in the vast majority of cases, and is unambiguous, distinguishing it clearly from uterine serous carcinoma and ovarian low-grade serous carcinomas. A detailed protocol for primary site assignment is presented for different scenarios, which is easy to follow and has been developed with a view to promoting a uniform approach worldwide.
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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 | 0.098 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".