Quality of tuberculosis care in high burden countries: the urgent need to address gaps in the care cascade
Bibliographic record
Abstract
Despite the high coverage of directly observed treatment short-course (DOTS), tuberculosis (TB) continues to affect 10.4 million people each year, and kills 1.8 million. High TB mortality, the large number of missing TB cases, the emergence of severe forms of drug resistance, and the slow decline in TB incidence indicate that merely expanding the coverage of TB services is insufficient to end the epidemic. In the era of the End TB Strategy, we need to think beyond coverage and start focusing on the quality of TB care that is routinely offered to patients in high burden countries, in both public and private sectors. In this review, current evidence on the quality of TB care in high burden countries, major gaps in the quality of care, and some novel efforts to measure and improve the quality of care are described. Based on systematic reviews on the quality of TB care or surrogates of quality (e.g., TB diagnostic delays), analyses of TB care cascades, and newer studies that directly measure quality of care, it is shown that the quality of care in both the public and private sector falls short of international standards and urgently needs improvement. National TB programs will therefore need to systematically measure and improve quality of TB care and invest in quality improvement programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".