Rare tumors in gynaecological cancers and the lack of therapeutic options and clinical trials
Bibliographic record
Abstract
Introduction: Up to 50% of gynecological cancers can be considered rare according to the Surveillance of Rare Cancers in Europe (RARECARE) consortium definition of an incidence of less than 6 cases per 100 000 people. These cancers usually have a poor prognosis as they are often delayed in their diagnosis and treatment due to the lack of knowledge.Areas covered: This review briefly addresses the current state of management and the lack of effective treatment strategies for the most commonly seen rare gynecological malignancies. It also highlights the challenges surrounding attempts to harmonize treatment practices and the role of the international medical community.Expert opinion: Given their rarity, biological and clinical data are lacking for many gynecological cancers. Current efforts are on-going to improve care of these patients, including the development of international consortia, prospective databases with biobanking, acceptance of novel clinical trial design and education of the medical field as well as improvement of patient awareness.
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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 | 0.049 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".