Experience of implementing the integrated TB model in Zhejiang, China: a retrospective observational study
Bibliographic record
Abstract
BACKGROUND: This study aims to assess the implementation of the TB control program under the integrated model in China where TB diagnosis and treatment is provided in TB designated hospitals. METHODS: Six counties under the integrated model in Zhejiang were randomly selected. TB referral and tracing was analyzed based on routine TB reporting data between January and December 2009 from county TB dispensaries. Regarding treatment and community management, we conducted face-to-face surveys with 50 new TB patients randomly selected from each county, and reviewed their medical charts. RESULTS: A total of 7090 persons with presumptive TB were reported in 2009, of whom, 66.7% (4732/7090) were referred by other health facilities to TB designated hospitals, while 80.2% (3795/4732) were successfully referred. In total, 301 patients were surveyed and had a median medical expenditure of US$192. Ten percent (31/301) missed at least one dose during their treatment, and 64.5% (194/301) received direct observation, mostly by family members. CONCLUSIONS: The integrated model performed better on case referral and community management, but higher medical expenditures than those reported by studies under the dispensary model in China. Clear guidelines should be issued on supervising TB treatment in designated hospitals.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".