High-risk regional distribution of gynecopathy in China in 2020
Bibliographic record
Abstract
This paper mainly focuses on studying the regional distribution of the prevalence of six gynecological diseases across 31 provinces in China in 2020, followed by the description of prevalence in three representative regions in 2018-2020. As a result, offer suggested planning and reference frameworks at the regional level for China's prevention and care of women's health. By summarizing data from the 2021 China Health Statistics Yearbook report, a systematic cluster analysis was completed by factor analysis. Thirty-one provinces in China in 2020 were divided into three major categories according to the regional significance of women's cancer prevalence, and the clustering results showed that the prevalence of gynecological diseases in China had significant regional differences. The distribution of Chinese gynecological diseases is nonuniform. Governments should take targeted measures according to the differences in cluster results, which will help to develop various disease prevention and treatment strategies for reducing the risk of gynecological diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".