Registration of Malignant Head and Neck Malignant Tumors: 33 Years' Experience in a Single Prefecture
Bibliographic record
Abstract
新潟県では, 1986年より新潟県頭頸部悪性腫瘍登録委員会が新規症例の腫瘍登録を開始した. このたび本研究では, 頭頸部悪性腫瘍発生の経時変化を見ることを目的とし, 1986～2018年までの登録症例において年毎の頭頸部悪性腫瘍総数, 原発部位別数, 粗罹患率を分析し, さらにＴ分類別でＴ2以上症例に対するＴ1以下症例の比率 (Ｔ1以下／Ｔ2以上) を指標として検討した. 総数は12, 443例で, 男性8,619例 (69.3%), 女性3,824例 (30.7%) だった. 登録初年度と比べ2018年の年次毎症例数は3.7倍 (599例／160例) に増加し, 年平均増加率は4.2%で, 粗罹患率は4.3倍 (26.7/6.2) に増加していた. 年齢中央値は4歳 (64歳→68歳)上昇しており, 高齢化が粗罹患率上昇の要因と考えられた.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".