Analysis of the characteristics of national TB epidemic situation in schools from 2008 to 2012
Bibliographic record
Abstract
Objective To analyze the characteristics of TB in students. Methods We collected the pulmonary TB( PTB) reporting data from the Infectious Diseases Reporting System( IDRS),and TB outbreak investigating report from Emergency Public Health Events Reporting System,analyzed the distribution of reported PTB in students and the characteristics of TB outbreak. The total number of students in mainland China were 240 320 175 in 2008,238 547 238 in 2009, 237 235 870 in 2010,235 770 400 in 2011 and 235 770 400 in 2012 respectively. Results A total of 39 198 cases of PTB cases in students were reported in 2012 with the reported incidence of 16. 63/100 000,decreasing by 39. 28% in comparison with 2008( 65 815 cases,27. 39/100 000). In terms of the reported cases by quarter,the highest was in the second quarter,accounting for 30. 43%( 12 662/41 608)-32. 38%( 21 313/65 815) of the whole year. The top five school TB incidence provinces were Tibet( 79. 95/100 000,415/519 042),Qinghai( 59. 09/100 000,607/1 027 309),Guizhou( 36. 54/100 000,2956/8 089 905) and Chongqing( 33. 06/100 000,1778/5 377 413),Xinjiang( 26. 08/100 000,1082/4 147 979). Students PTB in 2012 accounted for 4. 12%( 39 198/951 508) in the whole patients,highest in 15-20 years group( 54. 12%, 21 215/39 198). From January 2009 to June 2013,a total of 21 cases of TB outbreak nationwide were reported in schools, 14 cases( 66. 67%) happened in senior high school with an average of 25 patients in each case. Conclusion From 2008 to 2012,the TB epidemic in schools had a declining trend by year,the reported incidence peaked in the second quarter and relatively high in the western provinces. The majority of TB cases were around 15-20 years old. The TB epidemic in schools in recent years revealed that some problems existed in TB control in schools,and we need to further strengthen TB control in schools.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".