Systematic review of latent tuberculosis infection and tuberculosis preventive therapy
Bibliographic record
Abstract
Objective To describe and evaluate the progress of the latent tuberculosis infection and tuberculosis preventive therapy.Methods We made a comprehensive search for published literatures including 7 databases,websites of 3 health agencies and Google Scholar.Studies related to the diagnosis of latent tuberculosis infection and tuberculosis preventive therapy were identified by inclusion and exclusion criteria.Initially 573 papers were searched out,and 17 papers were included eventually.Of the 17 papers included in the analysis,4 studies aimed to describe the status of latent tuberculosis infection.2 were guidelines published by US CDC,11 studies amid to evaluate the treatment regimen and effectiveness of preventive therapy.Results The Mycobacterium tuberculosis infection rate of the entire population of United States is about 4.2%,14.2% for non-Aboriginal population of Columbia of Canada,15.0% for the 8 provinces of Afghanistan,and our fourth epidemiological survey results showed this rate was 44.5% for all age group of China.(United States,Canada,Afghanistan use the tuberculin skin test induration diameter≥ 10 mm as standard of infection,and China use induration diameter≥ 6 mm as standard).Latent infection diagnosis is based on the comprehensive consideration of patients' past history,the tuberculin skin test or interferon-γ release test results,chest radiological examinations,physical examination.The effect of different regimen of preventive treatment varied greatly,with the protection rate ranging from 0% to 61%,the treatment completion rate from 43% to 90%,the incidence of adverse drug responses mainly rash,hepatitis,and peripheral neuritis from 0% to 10%.Conclusion The current focus of TB control in China is to achieve the purpose of rapid and effective control of TB transmission through the detection and cure of infectious cases.However,with the development of China's economy and the intensified TB case detection,tuberculosis preventive therapy will become an important part of TB control measures.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".