Update in Tuberculosis and Nontuberculous Mycobacterial Disease 2012
Bibliographic record
Abstract
In 2012, new publications in the Journal described both the predictive value of the new IFN-γ release assays for diagnosis of latent tuberculosis (TB), but also provided evidence that these new tests cannot be interpreted simply as positive or negative, as initially hoped. Surgical masks can reduce transmission of TB infection, but other measures such as state-wide implementation of targeted testing and treatment of latent TB or active case finding require substantial and sustained effort to successfully reduce TB morbidity and mortality. A quasiexperimental study revealed that a package of social interventions could substantially reduce risk of TB disease in heavily exposed (and infected) children in the preantibiotic era. A study in a high-TB burden setting suggested that a new rapid drug-susceptibility test for TB may be more practical for implementation than traditional culture-based phenotypic tests. And two studies of TB vaccines revealed that currently used bacillus Calmette-Guérin strains vary in their ability to affect correlates of immunogenicity, whereas a new candidate vaccine, MVA85A, was safe and immunogenic in adults. Studies of nontuberculous mycobacteria (NTM) described a rapid rise in the prevalence and spatial clustering of NTM in the United States over the past decade. Although risk factors for pulmonary NTM such as advanced age and low BMI are known, the mechanisms underlying infection and disease remain mysterious. Four studies of therapy of NTM disease highlighted the pressing need for well-designed international randomized controlled trials to improve our management of NTM disease.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".