Bibliographic record
Abstract
Ovarian lesions composed of spindle cells comprise a heterogeneous group; most are neoplastic but several non-neoplastic conditions are also composed of spindle cells. This review discusses the main differential diagnoses of an ovarian spindle cell lesion, especially concentrating on the recent literature. The majority of ovarian spindle cell lesions fall into the broad category of fibromatous neoplasms whereas others in the sex cord-stromal group may also be composed of spindle cells, including thecomas, granulosa, and Sertoli-Leydig cell tumors and rarer neoplasms, such as sclerosing stromal tumor and signet-ring stromal tumor. In the recent past there have been several major contributions on various aspects of ovarian spindle cell lesions, including cellular and mitotically active cellular fibromatous lesions, smooth muscle neoplasms, and metastatic gastrointestinal stromal tumors. Other mesenchymal or epithelial tumors and mixed epithelial and mesenchymal neoplasms may also enter into the differential diagnosis of an ovarian spindle cell lesion. Several non-neoplastic lesions may be composed of spindle cells, including massive edema, ovarian fibromatosis, stromal hyperplasia, and stromal hyperthecosis. Morphology remains the mainstay in diagnosis but immunohistochemistry may be invaluable in certain circumstances, one example being the identification of a metastatic gastrointestinal stromal tumor within the ovary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".