Intervening along the spectrum of tuberculosis: meeting report from the World TB Day nanosymposium in the Institute of Infectious Disease and Molecular Medicine at the University of Cape Town
Bibliographic record
Abstract
Tuberculosis (TB), caused by the highly infectious Mycobacterium tuberculosis , remains a leading cause of death worldwide, with an estimated 1.6 million associated deaths reported in 2017. In South Africa, an estimated 322,000 (range 230,000-428,000) people were infected with TB in 2017, and a quarter of them lost their lives due to the disease. Bacille Calmette-Guérin (BCG) remains the only effective vaccine against disseminated TB, but its inability to confer complete protection against pulmonary TB in adolescents and adults calls for an urgent need to develop new and better vaccines. There is also a need to identify markers of disease protection and develop novel drugs. It is within this backdrop that we convened a nanosymposium at the Institute of Infectious Disease and Molecular Medicine at the University of Cape Town to commemorate World TB Day and showcase recent findings generated by early career scientists in the institute. The speakers spoke on four broad topics: identification of novel drug targets, development of host-directed drug therapies, transmission of TB and immunology of TB/HIV co-infections.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".