Whole genome sequencing of M. tuberculosis for disease control in high-burden settings: study protocol for a cluster randomized controlled trial evaluating different community-wide intervention strategies in rural Madagascar
Bibliographic record
Abstract
BACKGROUND: Retrospective and descriptive molecular epidemiology studies have shown that Mycobacterium tuberculosis whole genome sequencing can identify outbreaks and disease transmission events with higher resolution than conventional epidemiological investigations. Those studies have strengthened our understanding of genomic polymorphisms correlating with person-to-person transmission and helped resolve putative transmission clusters. To date, systematic genomic surveillance programs implemented for M. tuberculosis were only implemented in low-incidence settings. The purpose of this study is to determine whether there is an impact of routine M. tuberculosis whole genome sequencing on tuberculosis case detection in a high-incidence setting. METHODS: A cluster randomized controlled trial will be performed. Forty-eight rural village groups (or Fokontany) in the Vohibato district of Madagascar will be randomized to one of three interventions arms. Arm 1 (standard of care) involves healthcare facility-based passive case detection with smear microscopy testing. Arm 2 (best practice) consists of active case finding and Xpert MTB/RIF Ultra PCR testing followed by household contact investigations. Arm 3 (novel intervention) includes the same interventions as arm 2, with addition of sputum culture and M. tuberculosis whole genome sequencing for all newly diagnosed cases. In arm 3, molecular suggested putative outbreaks are investigated, and additional TB suspects are appropriately tested. The intervention observational period will be 2 years. The primary outcome will be the number of detected cases/100,000/year in each arm after 1 year of intervention. DISCUSSION: This study is designed to determine whether there is an impact of prospective whole genome sequencing-based molecular typing on tuberculosis case detection in high-incidence settings. Investigating potential outbreaks and focusing active case finding in spatiotemporal settings where disease transmission is suggested by genomic typing is hypothesized to improve case detection in rural communities. TRIAL REGISTRATION: ClinicalTrials.gov NCT05406453 . Retrospectively registered on June 6, 2022.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".