Bibliographic record
Abstract
Because little was known concerning the scope of treatment of latent tuberculosis infection (LTBI) in the United States and Canada, identification of the types of clinics that administered such treatment, and patients who received it, would guide resource utilization and improve treatment initiation and completion. Sterling and coworkers, on behalf of the Tuberculosis Epidemiologic Studies Consortium, Centers for Disease Control and Prevention, surveyed 244 clinics, each having initiated LTBI treatment for 10 or more patients in 2002, at 19 U.S. and 2 Canadian sites (1). An estimated 37,857 patients started LTBI treatment in 2002, including 37,145 from the U.S. sites, with 79% at general public health clinics, 6.4%at immigrant/refugee clinics, and 6.1% at correction/detention facilities. Study catchment areas for the 19 U.S. sites represented 8.6% of the U.S. population and 12.7% of all tuberculosis (TB) cases in 2000. On extrapolation to the entire U.S. population, the estimated total number of LTBI treatment starts was approximately 291,000 to 433,000. Assuming a 5% lifetime risk of TB without treatment, and 20 to 60% treatment effectiveness, approximately 4,000 to 11,000 cases of TB were prevented in the United States. Thus, Sterling and coworkers concluded that treatment for LTBI was initiated among a substantial number of persons in theUnited States andCanada, primarily in the public sector, and such treatment could significantly decrease the disease burden in these countries. Targeted screening and treatment of latently infected subjects are central to strategies aimed at eliminating TB. Unfortunately, there appear to be few specific criteria, other than medical factors, in designating groups as high risk for developing TB. Moonan and coworkers conducted location-based screenings in partnership with multiple community-based organizations in communities previously demonstrated by geographic information system to have genotypically clustered Mycobacterium tuberculosis isolates (2). One person with TB was found for every 83 screened, and one person with LTBI for every five screened, far exceeding the expected yield of untargeted screening for a county with a TB incidence of only 5.7 per 100,000. Male subjects were more commonly identified (odds ratio [OR], 4.8). Thus, it appeared that combining genotyping and geographic information systems could potentially help in identifying high-risk status and in determining areas for location-based TB screening. In an editorial accompanying Moonan and colleagues’ article (2), it was pointed out that these data could be used as a tool for garnering the critical support of community-based organizations (3). Both shortand long-term benefits of such partnerships as well as the resulting interventions are important to measure. In addition to following up on the outcome of the intervention, an analysis of the services received by the screened individuals would clarify the relative roles of housing, correctional care
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.082 | 0.060 |
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".