Sertoli-Leydig cell tumour (SLCT) – the case of a 15 cm diameter ovarian tumour with negative markers and absent hormonal symptoms
Bibliographic record
Abstract
Sex cord-stromal tumours are a group of tumours derived from the stromal component of the ovary and testis that comprise granulosa, thecal, Sertoli, and Leydig cells as well as fibrocytes.Sertoli-Leydig cell tumours (SLCTs) are rare ovarian neoplasms, accounting for less than 1% of all cancers arising from this organ.They differ in size; some might be very small, while some of them are huge (from < 1 cm to 35 cm).Most SLCTs are unilateral and may be functionally diverse.Approximately one-fifth of SLCTs may contain various heterologous elements, e.g.gastric or intestinal-like epithelium or malignant parts.Women with SLCTs may suffer from hormonal disturbances, manifest high alpha-fetoprotein, CA125, and testosterone levels and may suffer from virilisation, oligomenorrhoea, hirsutism, acne, voice changes, clitoris hypertrophy, or alopecia.However, those symptoms do not occur in every case.Diagnosis of SLCTs is not easy, due to its rarity and varied presentation.Even though SLCTs are rare tumours, they should always be considered as a possibility, even in patients at different ages (typically 20-30 years old) without raised markers and hormonal disturbances.In this manuscript we report a case of SLCT, 15 cm in diameter, with low marker levels and lacking hormonal disturbances, found in a 36-year-old female.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".