[Bayesian statistics for translational medicine-from personal to public health].
Bibliographic record
Abstract
总结过去几十年的经验,美国国家卫生研究院(National Institute of Health,NIH)于2003年提出了转化医学(translational medicine)的概念,并将其作为一个全新的医学研究范式,试图在基础研究与临床医疗之间建立更直接的联系,缩短从实验室到病床的距离[1].转化医学建立在基因组学、微芯片等生物科技及其他信息技术的基础上,为现代医学提供了一个全新的指导,近年来日益受到各国医学界的广泛关注.随着生物和医学数据的爆炸式增长和数据结构的日益复杂性,人们逐渐认识到生物统计在医学和公共卫生研究中的重要性.作为统计学习和知识挖掘的一个完备的理论体系,贝叶斯统计(Bayesian statistics)方法越来越受到各行业科研工作者的重视[2].本研究中笔者首先介绍贝叶斯统计的核心思想并阐述其在转化医学方法论构架中的核心地位,进而具体说明该统计方法在个人健康[3]和公共卫生[4]研究中的几个典型应用。
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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 | 0.023 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 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; 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".