Bibliographic record
Abstract
近年，根拠に基づいた医療（evidence based medicine）が日本の医療分野においても重要視されるようになった．ランダム化比較試験は根拠に基づく医療を支える重要な柱のひとつである．しかし残念ながらその研究デザインの不備や悪意をもった解析によってランダム化比較試験結果が真実とは異なった形で報告されることがしばしば起こりえる．そのため論文を批判的に読み，目の前の患者に適用可能か否かを判断する論文リテラシーが臨床家に求められている．本稿ではCONSORT声明2010の解説を踏まえ，ランダム化比較試験論文をどのように読解するか実例をあげて解説する．
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 | 0.580 | 0.890 |
| Meta-epidemiology (narrow) | 0.007 | 0.010 |
| Meta-epidemiology (broad) | 0.037 | 0.026 |
| Bibliometrics | 0.021 | 0.010 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.029 | 0.015 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.027 | 0.041 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".