Complexities and benefits of adopting next-generation sequencing-based tuberculosis diagnostics: a qualitative study among stakeholders in low and high-income countries
Bibliographic record
Abstract
OBJECTIVES: next-generation sequencing implementation in Madagascar and Canada, towards informing implementation of this diagnostic technology in public health agencies and clinical settings in and beyond these settings. DESIGN: This qualitative study involved conducting semistructured interviews with key stakeholders engaged with next-generation sequencing implementation in Madagascar and Canada. Team-based descriptive analysis supported by Nvivo V.12.0 was used to identify key themes. SETTING: The study was conducted with participants involved at the clinical, diagnostic and surveillance levels of tuberculosis (TB) management from Madagascar and Canada. PARTICIPANTS: Eighteen participants were interviewed (nine Madagascar and nine Canada) and included individuals purposively sampled based on involvement with TB surveillance, laboratory diagnosis and clinical management. RESULTS: The following five themes emerged in the analysis of Malagasy and Canadian interviews: (1) heterogeneity in experience with established TB diagnostics, (2) variable understanding of new sequencing-based diagnostics potential; (3) further evidence as being key to expand adoption; (4) ethical arguments and concerns; (5) operational and system-level considerations. CONCLUSION: There persists important lack of familiarity with TB next-generation sequencing (TB NGS) applications among stakeholders in Canada and Madagascar. This translates into skepticism on the evidence underlying its use and its true potential value added within global public health systems. If deployed, TB NGS testing should be integrated with clinical and surveillance programmes. Although this is perceived as a priority, leadership and funding responsibilities for this integration to happen remains unclear to clinical, laboratory and public health stakeholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".