Surveillance and analysis of information of public health emergencies in 2008 and 2010 in Tianjin
Bibliographic record
Abstract
[Objective]To analyze the epidemiological features of public health emergencies in 2008 and 2010 in Tianjin,explore the causes of emergencies,and provide the technical support of monitoring and early-warning of public health emergencies.[Methods]Using the retrospective investigation and network information management system of public health emergencies,the data of public health emergencies occurred in 2008 and 2010 in Tianjin were collected and analyzed.[Results]A total of 117 public health emergencies were reported by network information management system of public health emergencies in 2008 and 2010,which included 3 events of grade Ⅲ(2.56%),92 events of grade Ⅳ(78.63%) and 22 ungraded events(18.80%).There were 92(78.63%) infectious disease events,14(11.97%) food poisoning events,5(4.27%) environmental factors events,1(0.85%) occupational poisoning event and 5(4.27%) other events.44.44% of events occurred in primary and middle schools,followed by kindergartens(21.37%).The peak season was the second quarter,which accounted for 38.46% of total events,and number of events in rest three quarters accounted for 19.66%,23.93% and 17.95% respectively.There were 3 884 patients in 117 events,and 13 patients died.47 events were reported in urban area,22 in new coastal region,19 in villages and towns and 29 in rural area.[Conclusion]The public health emergencies in Tianjin are mainly infectious disease and food poisoning,and most of events occurred in schools and kindergartens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".