Bibliographic record
Abstract
Tuberculosis still remains a leading infectious cause of death worldwide, although the BCG vaccine has been used for 80 years. There is an urgent need to develop improved BCG or new tuberculosis vaccines. This apparently represents a daunting task, since it will take a long time before a vaccine can be declared to be better than the current BCG vaccine, both in experimental and human studies. The current review takes a brief historic look at the use of current BCG vaccine and provides an overview on what are considered to be the key immunologic criteria that have to be met by a new generation of tuberculosis vaccines. It also provides the most up-to-date information on the latest developments in tuberculosis vaccine research, with a focus on mycobacterial organism-based and Mycobacterium tuberculosis antigen-based vaccines. Consideration is also given to the mucosal route of immunization and 'prime and boost' regimens. This review also presents several important tables, highlighting critical components of antituberculosis immunity, the most commonly tested immune adjuvants, the types of novel tuberculosis antigen-based vaccines and the outcome of different heterologous 'prime and boost' vaccination regimens.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".