Perspectives from the 2 <sup>nd</sup> International Post-Tuberculosis Symposium: mobilising advocacy and research for improved outcomes
Bibliographic record
Abstract
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 | 0.078 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.022 | 0.034 |
| Insufficient payload (model declined to judge) | 0.014 | 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".