Results of endometrial cytology using slides prepared by saline washing method
Bibliographic record
Abstract
目的 : 癌の見落としである子宮内膜細胞診の偽陰性を減らす.方法 : 採取器具はエンドサイトないしソフトサイトを用い, 石井らの提唱した生食洗浄法にて処理した. 細胞診所見では集塊を重視し, 樹枝状, 乳頭状, 不整形の集塊をチェックした. 2007 年からの 5 年間の成績を検討した.成績 : 材料適切な 1680 件の内訳は悪性 118 件 7.0%, 悪性疑い 26 件 1.5%, 鑑別困難 95 件 5.7%, 良性 1441 件 85.8%であった. 細胞診にて悪性と判定した 118 件中の 116 件に組織診断が施行され, 全例組織診で癌 (癌肉腫を含む) が確認され, 偽陽性はなかった. 悪性疑いの 26 件中 25 件に組織診断が施行され, 癌は 23 件で, 1 件は異型増殖症で, 1 件のみが腫瘍なしであった. 組織診で確認された子宮体癌 (癌肉腫を含む) は 107 例で, その初回細胞診は悪性 76 例 71.0%, 悪性疑い 13 例 12.2%, 鑑別困難 13 例 12.2%で, 鑑別困難以上に判定できたのは 95.1%であり, 偽陰性は 4.6%であった. 偽陰性例は再検討した結果, 全例サンプリングエラーと考えられた.結論 : 生食洗浄法は内膜細胞診の処理法として優れている.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".