Epidemiological analysis of tuberculosis cases in a comprehensive hospital from 2010 to 2012
Bibliographic record
Abstract
Objective To analyze epidemiological feature of tuberculosis cases in a comprehensive hospital from 2010 to 2012, to provide foundation for effective control and management of tuberculosis cases. Methods Epidemic data of tuberculosis cases in the hospital from 2010 to 2012 were downloaded from Chinese Information System for Diseases Control and Prevention. Descriptive epidemiological method was used to analyze time distribution, regional and population distribution.Results The male patients with pulmonary tuberculosis were more than the female. The patients of three age groups50-year-old, 60-year-old, 70-year-old accounted for 15.16%, 22.90% and 16.31%, respectively; the patients of farmers and herdsmen accounted for 29.80%, and the retired accounted for 20.65%. The patients from other areas of Xinjiang accounted for 69.09%, while the patients in Urumqi accounted for 26.57%. But there was no statistical difference among each quarter and each year(χ2=12.56,P=0.05). Among the patients, detection rate of the phlegm was 67.31%, with positive rate of18.98%. Conclusions The targeted management measures of tuberculosis cases is set down based on the patients' gender,age, occupation, region and classification. The finding and management of tuberculosis cases should be strengthened among the elderly, farmers and herdsmen and students. Early discovery, control and treatment will help to improve the prevention level of tuberculosis cases in Xinjiang.
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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".