Mucinous cystic tumour of low malignant potential presenting in a patient with prior non-seminatous germ cell tumour
Bibliographic record
Abstract
Urachal neoplasms are rare entities which may be classified as cystic or non-cystic. Literature surrounding patient outcomes remains limited to non-cystic, urachal adenocarcinomas. Literature focusing on mucinous cystic neoplasms of the urachus is sparse. These mucinous cystic lesions may be subclassified as benign mucinous cystadenomas, mucinous cystic tumours of low malignant potential, and mucinous cystadenocarcinomas. Mucinous subtypes have the potential to behave aggressively and may result in pseudomyxoma peritonei. We describe here the case of a 37-year-old male with a mucinous cystic tumour of low malignant potential after prior right orchiectomy and left hydrocelectomy. This case raises the interesting possibility of multiple genitourinary neoplasms arising in a similar time frame.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".