Shanghai Sixth People's Hospital: Challenges in Diabetes Care Equalization
Bibliographic record
Abstract
上海市第六人民医院前院长贾伟平从医43年，一直致力于糖尿病精准诊疗、预警筛查、发病机制的研究及防治工程管理。案例按照从小到大、从局部到全局的顺序，讲述贾伟平从糖尿病的尖端科研开始，走通从个体诊治到群体防控的道路，从上海最基层的社区出发，进而覆盖上海全市，进而走向全国，领导中国基层糖尿病的防治管理工作，走通从大城市到县乡村的医防融合的同质化道路。案例着重描述了数字技术在普惠医疗实现过程中扮演的角色。 2021年11月，贾伟平当选中国工程院院士，感觉到更大的责任，她在思考如何利用好院士这个更高更广阔的舞台，将已被实践验证的模式推广到更需要的中国广大基层。
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".