Study on comprehensive measures for improving early detection of TB infection source
Bibliographic record
Abstract
Objective To explore the early case detection model of tuberculosis infection source by way of a series of measures carried out in the demonstration zone.Methods Several activities were carried out in Qiaokou and Jianghan districts of Wuhan,including: community health health education,clue survey,key population screening,smear-negative tuberculosis sputum culture,regulating tuberculosis epidemic report and referral of general hospitals etc.The data were collected and analyzed by using of EXCEL and SAS8.1 software.Results In the first quarter of 2011,the number of consulting TB suspects reached 7065 in these two districts,far more than that before intervening.The primary visiting rate gone up to 20.48‰.The referral arrival rate and overall arrival rate were 81.15% and 99.23% respectively.Through clue survey,30077 TB suspects were detected,accounting for 3.88% of the total screening population.Among the high risk population receiving TB screening,169 TB suspects were detected and the detection rate was 2.71%,of which there were two multi-drug resistant cases.Besides,the sputum specimens of 175 initial treated smear-negative TB patients were cultured,and the result showed that 15 cases were positive and 160 negative.Among the 137 Smear positive TB cases detected during the first quarter of 2011,case consulting delay and doctor diagnosis delay were 32.85% and 22.63% respectively,the overall delay rate was 23.36%,while the early detection rate was 76.64%.Conclusions Taking comprehensive measures can early discover TB patients,however,powerful fund support is also crucial for its sustainable and intensive development.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".