The Effect of Temperature Difference in the Same Quarter on Blood Biochemical Levels in Patients with Cerebral Infarction in Northeast China and Hainan
Bibliographic record
Abstract
Introduction: The study examines the impact of temperature differences within the same season on blood biochemical levels in cerebral infarction patients in Northeast China and Hainan. To study the effect of temperature differences in the same season on blood biochemical levels in patients with cerebral infarction in Northeast China and Hainan. Methods: A total of 393 patients with cerebral infarction in a certain area of Northeast China and 343 patients with cerebral infarction in a certain area of Hainan were selected from November 2021 to March 2022, and then the general medical history data and blood biochemical test results of patients with cerebral infarction were collected. A binary logistic regression analysis was performed on the data. Results: In the same quarter, there was a significant correlation between cerebral infarction in patients in Northeast China and Hainan (OR = 0.034, p = 0.000). Gender, smoking, drinking, hypertension, diabetes, coronary heart disease, and triglycerides are high risk factors for cerebral infarction. Conclusion: The incidence of cerebral infarction in patients in Northeast China and Hainan was significantly associated within the same quarter.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".