The Association of Air Pollution With the Patients’ Visits to the Department of Respiratory Diseases
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to evaluate the impact of air particulate matter 2.5 (PM2.5) on the daily number of patients' visits to the Department of Respiratory Diseases in a local general hospital. METHODS: The number of patients in outpatient department of respiratory diseases (ODRD) in a general hospital of Jinan, China, the air quality and meteorological data were collected for 1 year. By controlling the confounding factors such as "day of the week" effects and the meteorological factors, the generalized additive Poisson regression analysis was conducted to evaluate the impact of PM2.5 on the number of patients' visits to the ODRD. RESULTS: Within two consecutive days, if the cumulative PM2.5 was less than 200 µg/m(3), the daily number of patients in the ODRD did not increase significantly; however, it increased dramatically when the concentration of PM2.5 particles reached the range between 200 and 400 µg/m(3). CONCLUSION: There is a non-linear relationship between the concentration of atmospheric PM2.5 particles and the daily number of patients in the ODRD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".