Proceedings from the Third International Post-Tuberculosis Symposium: expanding the circle
Bibliographic record
Abstract
In light of the recent growth in interest and knowledge of post-TB sequelae, there were high levels of engagement during the 3rd International Post-Tuberculosis Symposium held in Stellenbosch, South Africa. This multi-disciplinary symposium aimed to: 1) Advocate for greater global awareness of post-TB sequelae and empower TB-affected communities; 2) Advance knowledge by sharing current evidence and identifying key priorities; 3) Foster collaborations by strengthening research networks and developing concrete plans for research driven advocacy; and 4) Advance the field by establishing areas of consensus around diagnosis, care, and management. Guided by a 14-member Steering Committee, 9 academic working groups came together to develop key content for plenary sessions and facilitated workshops related to: Patient Engagement, Epidemiology and Modelling, Pathogenesis, Post-TB Lung Disease; Cardiovascular and Pulmonary Vascular Disease; Central Nervous System and Musculoskeletal Disease; Paediatrics Economic; Social and Psychological Sequelae; and Advocacy, Policy, and Stakeholder Engagement. Each group outlined progress within their respective fields and defined key priorities to focus discussion. The Symposium further catalysed coordinated action for the post-TB community of patients, advocates, clinicians, and researchers to define a clear path towards improving outcomes, reducing inequities, and ensuring TB survivors receive the care and support they deserve.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.056 | 0.019 |
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".