Improving measurement of tuberculosis care cascades to enhance people-centred care
Bibliographic record
Abstract
Care cascades represent the proportion of people reaching milestones in care for a disease and are widely used to track progress towards global targets for HIV and other diseases. Despite recent progress in estimating care cascades for tuberculosis (TB) disease, they have not been routinely applied at national and subnational levels, representing a lost opportunity for public health impact. As researchers who have estimated TB care cascades in high-incidence countries (India, Madagascar, Nigeria, Peru, South Africa, and Zambia), we describe the utility of care cascades and identify measurement challenges, including the lack of population-based disease burden data and electronic data capture, the under-reporting of people with TB navigating fragmented and privatised health systems, the heterogeneity of TB tests, and the lack of post-treatment follow-up. We outline an agenda for rectifying these gaps and argue that improving care cascade measurement is crucial to enhancing people-centred care and achieving the End TB goals.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".