Bibliographic record
Abstract
[Objective]To understand the TB patients data via TB special reporting system,to analyze the dynamic changes of epidemic reporting and epidemiological characteristics,and provide evidence for the future TB control programs.[Methods]Descriptive analysis was performed on TB patients data reported in TB special reporting system from 2007-2011.[Results]A total of 18 187 TB cases were reported from 2007-2011,with average reported incidence of 71.52 /100 000.The incidence showed annual declining trend,from 87.5 /100 000 in 2007 to 58.96 /100 000 in 2011.Smear positive occupied 43.03%,with upward trend.Male cases were more than female cases,with ratio of 3.09∶ 1.TB cases distributed in every month,the incidence was relatively low in 1st quarter and 4th quarter.The incidence of counties with convenient transportation was higher than that of remote mountain area,with the highest(101.61 /100 000) in Lipu County and the lowest(48.17 /100 000) in Ziyuan County,showing upward tendency in urban area.[Conclusion]Guiling City actively promotes the modern TB control strategy,strengthen DOTS management to reduce the TB incidence.But the TB epidemic is still not optimistic,effective TB prevention and management measures should be adopted to control the spread of TB,aiming at different populations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".