Bibliographic record
Abstract
ICTの利活用が進みさまざまな分野でビックデータの重要性が指摘されている。日本腎臓学会は厚生労働省臨床効果データベース事業として，日本医療情報学会と共同し新規全国規模の包括的慢性腎臓病（CKD）臨床効果情報データベース（J-CKD-DB）の構築に成功した。SS-MIX2を活用し，電子カルテ情報からCKD該当例のデータ（患者基本情報，処方，検査値等）を自動抽出しデータベース化するものである。本データベース構築においては①検査方法の差異，②検査結果の単位の違い，③ローカルコードの汎用など施設間で多くの調整が必要であった。これらを克服し現在15万人程度のCKDデータベースを構築することに成功した。本稿ではJ-CKD-DBの現況と今後の展望について述べたい。
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".