Bibliographic record
Abstract
低酸素誘導因子（hypoxia-inducible factor；HIF）は，細胞の低酸素応答を担う主要な転写因子である。HIFの活性化は内因性のエリスロポエチン（erythropoietin；EPO）産生や，体内での鉄利用を最適化することでヘモグロビン（Hb）値を上昇させる。そのためHIF活性化薬は腎性貧血の新規治療薬として，現在臨床第Ⅱ相・第Ⅲ相試験まで進んでおり，赤血球造血刺激因子製剤（erythropoiesis stimulating agent；ESA）使用による副作用や高コストを回避し，ESA低反応性貧血を改善させることが期待されている。さらにHIF活性化薬は，さまざまな動物実験で，急性腎障害（acute kidney injury；AKI）を軽減させることや，肥満や糖・脂質異常症にも有益である可能性が報告されている。今後，網膜症や悪性腫瘍の進展を中心とした副作用に対する安全性や，長期使用の有効性についての検討が必要である。
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".