Did FIDELIS projects contribute to the detection of new smear-positive pulmonary tuberculosis cases in China?
Bibliographic record
Abstract
Setting: The first phase of the Fund for Innovative DOTS Expansion through Local Initiatives to Stop TB (FIDELIS) projects in China started in 2003. Objective: To determine whether the FIDELIS projects contributed to the increased case detection rate for new smear-positive pulmonary tuberculosis (PTB) in China. Methods: We compared the case notification rates (CNRs) in the intervention year with those of the previous year in the FIDELIS areas, then compared the difference between the CNRs of the intervention year and the previous year in the FIDELIS areas with those in the non-FI-DELIS areas within the province. Results: There was an increase in the CNR in the intervention year compared with the previous year for all the project sites. The differences between the CNR in the intervention year and the previous year ranged from 6.4 to 31.1 per 100 000 population in the FIDELIS areas and from 2.9 to 20.4/100 000 in the non-FIDELIS areas. Differences-in-differences analysis shows that the differences in the CNRs in the FIDELIS areas were not statistically significantly different from those in the non-FIDELIS areas ( P = 0.393). Conclusion: The FIDELIS projects may have contributed to the increase in case detection of new smear-positive PTB in China, but the level of evidence is low.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".