End-point definition and trial design to advance tuberculosis vaccine development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tuberculosis (TB) remains a leading infectious cause of death worldwide and the coronavirus disease 2019 pandemic has negatively impacted the global TB burden of disease indicators. If the targets of TB mortality and incidence reduction set by the international community are to be met, new more effective adult and adolescent TB vaccines are urgently needed. There are several new vaccine candidates at different stages of clinical development. Given the limited funding for vaccine development, it is crucial that trial designs are as efficient as possible. Prevention of infection (POI) approaches offer an attractive opportunity to accelerate new candidate vaccines to advance into large and expensive prevention of disease (POD) efficacy trials. However, POI approaches are limited by imperfect current tools to measure Mycobacterium tuberculosis infection end-points. POD trials need to carefully consider the type and number of microbiological tests that define TB disease and, if efficacy against subclinical (asymptomatic) TB disease is to be tested, POD trials need to explore how best to define and measure this form of TB. Prevention of recurrence trials are an alternative approach to generate proof of concept for efficacy, but optimal timing of vaccination relative to treatment must still be explored. Novel and efficient approaches to efficacy trial design, in addition to an increasing number of candidates entering phase 2–3 trials, would accelerate the long-standing quest for a new TB vaccine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it