A rare case report of ovarian juvenile granulosa cell tumor with massive ascites as the first sign, and review of literature
Bibliographic record
Abstract
RATIONALE: Massive ascites as the first sign of ovarian juvenile granulosa cell tumor (JGCT) in an adolescent is an extremely rare, and its clinical features and treatment methods have not been well described. PATIENT CONCERNS: The clinical characteristics, diagnosis, and treatment methods in a 19-year-old girl who presented with massive abdominal distention and ascites was retrospectively reviewed. Abdominopelvic ultrasonography showed a large amount of ascites. The nature of ascites was exudate. All tumor markers were normal, but ascites and serum tumor CA125 levels were significantly increased. Abdominal CT showed left attachment area teratoma and right attachment area capsule solid change. DIAGNOSES: Histological and immunohistochemical results were compatible with JGCT. Based on the FIGO classification, the patient with only malignant ascites was categorized into stage IC. INTERVENTIONS: The patient underwent mass resection with salpingoophorectomy. Following the operation, she received 6 courses of adjuvant chemotherapy with Nedaplatin and Paclitaxel liposome. OUTCOMES: The patient was followed up postoperatively for 6 months to date without recurrence. LESSONS: We should be highly vigilant the JGCT with massive ascites as the first clinical manifestation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".