Moving toward Tuberculosis Elimination. Critical Issues for Research in Diagnostics and Therapeutics for Tuberculosis Infection
Bibliographic record
Abstract
Tuberculosis (TB) has surpassed HIV to become the leading infectious killer of adults globally, causing almost 2 million deaths annually (1). Although this airborne disease has been treatable since 1948, global rates of TB have dropped less than 2% per year; an estimated 10 million incident cases continue to occur annually, including 1 million in children (1, 2). Although transmission of active disease is an important driver of the epidemic, the seedbed that feeds the epidemic is the more than 2 billion people estimated to have TB infection, 5% to 10% of whom will progress to active disease during their lifetime (3). Although any successful strategy aimed at TB elimination needs to address this reservoir of TB infection worldwide, much remains to be understood about host and pathogen factors that can be used to identify increased risk for progression to disease and intervened on to prevent progression from occurring (4).
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.051 | 0.016 |
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".