[Analysis of the changes and characteristics of pediatric outpatient visits in a general hospital in Beijing before and after the COVID-19 pandemic].
Bibliographic record
Abstract
OBJECTIVE: To analyze the changes and characteristics of pediatric outpatient visits in a general hospital before and after the coronavirus disease (COVID-19) epidemic. METHODS: Based on the registration data of pediatric outpatient visits in the information system (HIS)of Beijing Tsinghua Changgung Hospital, from January 1 2018 to December 31 2020, aged 0 to 16 years, we analyzed the changes of outpatient visits before and after the epidemic, focusing on respiratory infection including influenza. The relationship between the outpatient visits and age and quarterly distribution were also studied. RESULTS: < 0.05). There were different distributions of influenza visits throughout 2018 and 2019, while it was only distributed in the first quarter and 99% in January in 2020. CONCLUSION: The respiratory infection and influenza visits have decreased significantly in our pediatric outpatient department after the COVID-19 epidemic, which is considered closely related to the lifestyle and personal protection after the epidemic. It is recommended that health education on respiratory infection and influenza prevention should be strengthened, especially in winter and spring, to promote the development of good respiratory and hand hygiene habits.
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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 | 0.001 | 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".