Implications of subclinical tuberculosis for vaccine trial design and global effect
Bibliographic record
Abstract
Tuberculosis is a leading cause of death from an infectious agent globally. Infectious subclinical tuberculosis accounts for almost half of all tuberculosis cases in national tuberculosis prevalence surveys, and possibly contributes to transmission and might be associated with morbidity. Modelling studies suggest that new tuberculosis vaccines could have substantial health and economic effects, partly based on the assumptions made regarding subclinical tuberculosis. Evaluating the efficacy of prevention of disease tuberculosis vaccines intended for preventing both clinical and subclinical tuberculosis is a priority. Incorporation of subclinical tuberculosis as a composite endpoint in tuberculosis vaccine trials can help to reduce the sample size and duration of follow-up and to evaluate the efficacy of tuberculosis vaccines in preventing clinical and subclinical tuberculosis. Several design options with various benefits, limitations, and ethical considerations are possible in this regard, which would allow for the generation of the evidence needed to estimate the positive global effects of tuberculosis vaccine trials, in addition to informing policy and vaccination strategies.
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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 | 0.189 | 0.404 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".