Small-Cell Carcinoma of the Ovary, Hypercalcemic Type–Genetics, New Treatment Targets, and Current Management Guidelines
Bibliographic record
Abstract
, which encodes for the SMARCA4 protein (BRG1), a subunit of the SWI/SNF chromatin remodeling complex. Approximately 20% of human cancers possess pathogenic variants in at least one SWI/SNF subunit. Because of their role in regulating many important cellular processes including transcriptional control, DNA repair, differentiation, cell division, and DNA replication, SWI/SNF complexes with mutant subunits are thought to contribute to cancer initiation and progression. Fewer than 500 cases of SCCOHT have been reported in the literature and approximately 60% are associated with hypercalcemia. SCCOHT primarily affects females under 40 years of age who usually present with symptoms related to a pelvic mass. SCCOHT is an aggressive cancer, with long-term survival rates of 30% in early-stage cases. Although various treatment approaches have been proposed, there is no consensus on surveillance and therapeutic strategy. An international group of multidisciplinary clinicians and researchers recently formed the International SCCOHT Consortium to evaluate current knowledge and propose consensus surveillance and therapeutic recommendations, with the aim of improving outcomes. Here, we present an overview of the genetics of this cancer, provide updates on new treatment targets, and propose management guidelines for this challenging cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".