Mucinous Ovarian Tumors of Mullerian-type: An Analysis of 17 Cases Including Borderline Tumors and Intraepithelial, Microinvasive, and Invasive Carcinomas
Bibliographic record
Abstract
Mullerian-type mucinous tumors (MMTs) of the ovary are characterized by a papillary architecture similar to that of serous tumors and a content of endocervical-like mucinous epithelium. The latter may be admixed with other mullerian-type epithelia, including those of serous, endometrioid, and squamous types, and indifferent eosinophilic epithelial cells. We analyzed 17 MMTs, including 12 borderline tumors, 2 intraepithelial carcinomas, 2 microinvasive carcinomas, and 1 invasive carcinoma. Fourteen of 16 tumors (88%) with available staging were stage I; the remaining two cases (both borderline) were stage IIa and IIIc. Endometriosis was identified in eight cases (47%). Only two patients (12%) had bilateral tumors, a frequency of bilaterality lower than in previous studies. Five patients (31%) had conservative treatment consisting of a cystectomy or unilateral salpingo-oophorectomy. All patients had a favorable outcome, with no recurrences or disease-related deaths, regardless of the presence of high mitotic index, intraepithelial carcinoma, microinvasion, bilaterality, conservative treatment, or advanced stage. This indolent behavior of MMTs is similar to that previously reported, but additional cases of invasive carcinomas in this category are needed to better define their outcome.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".