Extrarenal teratoma with nephroblastoma in the retroperitoneum
Bibliographic record
Abstract
RATIONALE: Teratoma with nephroblastoma is a rare disease. The most common site at which teratoma with nephroblastoma occurs is the kidney. The mechanisms underlying the development of teratoma with nephroblastoma have not been fully elucidated. PATIENT CONCERNS: In the current report, we describe the clinical characteristics of a 3-year-old girl with a complaint of a painless abdominal mass in the upper right side of the body. Ultrasonography and computed tomography revealed a cystic-solid mass with a clear boundary. DIAGNOSIS: Surgical resection and a subsequent pathological examination confirmed that the mass contained teratoma tissues and renal blastemal components, which supports the diagnosis of teratoma with nephroblastoma. INTERVENTIONS: The patient underwent an exploratory laparotomy through a transverse abdominal incision. Complete resection of the mass was performed in this patient. OUTCOMES: The patient's postoperative course was uneventful and she was discharged on the 8th postoperative day. The girl had no complaints during the 2 years follow-up period. LESSONS: Teratoma with nephroblastoma is a rare entity that typically presents in childhood. Due to its rarity, no standardized criteria have been established for the categorization and treatment of these lesions. However, a complete excision of this tumor allows the diagnosis to be confirmed and lowers the risk of recurrence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".