Do no harm - re-evaluating the risks of overtreatment in community-wide tuberculosis screening
Bibliographic record
Abstract
Background: Community-wide screening is a crucial strategy to end tuberculosis (TB), but a common concern is potential harm from overtreatment following false positive diagnoses. However, current reference standards determining test performance have limitations, with implications for prevalence thresholds and treatment decisions for community-wide screening. Methods: We estimated coverage of community-wide screening at a prevalence threshold of 0.5% (current global standard), 0.25%, and 0.1% for adult pulmonary TB. We considered test performance for Xpert Ultra against different reference standards (sputum culture, plus clinical evaluation, plus disease progression within two years). Potential harm was estimated through disability adjusted life years (DALYs) incurred or averted by treatment. We report net specificity, positive predictive value (PPV), the ratio of false positives to true positives, and DALYs averted for (non-)treatment based on different reference standards. Results: A lower threshold would increase screening coverage from the current 42% to 84% (0.25% threshold) and 89% (0.1% threshold) of the global TB burden. In a population of 100,000 with 0.5% prevalence, specificity was 99.5% for community screening, but increased to 99.7% using disease progression as reference standard, with PPV increasing from 45 to 66%. In addition, estimated harm of withholding appropriate treatment was approximately 1,200 times higher compared to providing inappropriate treatment, with treatment initiation after a positive Xpert Ultra increasing overall DALYs averted (median 5,977 versus 3,750). Discussion: The benefit of TB treatment following a positive molecular test in community-wide screening likely outweighs the harm associated with possible overtreatment, supporting expanding coverage of simplified community-wide screening.
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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 | 0.085 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".