Perspectives from the 2nd International Post-Tuberculosis Symposium: mobilising advocacy and research for improved outcomes
Bibliographic record
Abstract
In 2020, it was estimated that there were 155 million survivors of TB alive, all at risk of possible post TB disability. The 2 nd International Post-Tuberculosis Symposium (Stellenbosch, South Africa) was held to increase global awareness and empower TB-affected communities to play an active role in driving the agenda. We aimed to update knowledge on post-TB life and illness, identify research priorities, build research collaborations and highlight the need to embed lung health outcomes in clinical TB trials and programmatic TB care services. The symposium was a multidisciplinary meeting that included clinicians, researchers, TB survivors, funders and policy makers. Ten academic working groups set their own goals and covered the following thematic areas: 1) patient engagement and perspectives; 2) epidemiology and modelling; 3) pathogenesis of post-TB sequelae; 4) post-TB lung disease; 5) cardiovascular and pulmonary vascular complications; 6) neuromuscular & skeletal complications; 7) paediatric complications; 8) economic-social and psychological (ESP) consequences; 9) prevention, treatment and management; 10) advocacy, policy and stakeholder engagement. The working groups provided important updates for their respective fields, highlighted research priorities, and made progress towards the standardisation and alignment of post-TB outcomes and definitions.
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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 | 0.065 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.023 | 0.034 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".