A case of primary cervical carcinosarcoma
Bibliographic record
Abstract
背景:子宮頸部発生癌肉腫の報告はまれである. 術前診断に苦慮した1例を報告する.症例:58歳, 不正出血, 下腹痛を主訴に来院. 子宮頸部から筋腫分娩様に膣内に突出する, 易出血性の充実性腫瘍を認めた. 画像診断では子宮はほぼ一塊の腫瘤影, 頸部細胞診ではclass V・中分化程度の腺系悪性細胞の小集塊を多数認め, 頸部生検では扁平上皮, 腺系両方への分化が示唆される低分化癌と診断された. 摘出標本の病理組織所見では, 扁平上皮様の変化を伴う腺癌と, 一部に平滑筋肉腫, 骨肉腫, 軟骨肉腫の成分があり, 頸管腺領域を主体に漿膜直下まで浸潤していた.結論:診断に苦慮するような子宮悪性腫瘍診断の際には, 細胞診に癌肉腫細胞が出現していなくても, 本疾患の可能性を一考する必要があると考えられた.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".