Upfront Xpert MTB/RIF testing on various specimen types for presumptive infant TB cases for early and appropriate treatment initiation
Bibliographic record
Abstract
BACKGROUND: Diagnosis of tuberculosis (TB) in infants is challenging due to non-specific clinical presentations of the disease in this age-group and low sensitivity of widely available TB diagnostic tools, which in turn delays prompt access to TB treatment. Upfront access to Xpert/MTB RIF (Xpert) testing, a highly sensitive and specific rapid diagnostic tool, could potentially address some of these challenges. Under the current project, we assessed the utility and feasibility of applying upfront Xpert for diagnosis of tuberculosis in infants, including for testing of non-sputum specimens. METHODS: A high throughput lab was established in each of the four project cities, and linked to various health care providers across the city, through rapid specimen transportation and electronic reporting linkages. Free Xpert testing was offered to all infant (<2 years of age) presumptive TB cases (both pulmonary and extra-pulmonary) seeking care at public and private health facilities. RESULTS: A total of 7,994 presumptive infant TB cases were enrolled in the project from April 2014 to October 2016, detecting 465 (5.8%, CI: 5.3-6.4) TB cases. The majority (93.9%; CI: 93.4-94.4) of patient specimens were non-sputum and TB positivity was higher amongst non-sputum specimens. Further, a high proportion (5.6% CI 3.8-8.1) of infant TB cases were found to be rifampicin resistant. Covering large cities with a single lab per city over more than two years, the project demonstrated the feasibility of same-day diagnosis with upfront Xpert testing. This in turn led to prompt treatment initiation, with a two-day median turnaround time to treatment initiation. Case mortality observed in the project cohort of diagnosed TB cases was 11.0% (CI 8.4-14.1), the majority of which was pre- or early treatment mortality, in spite of prompt access to treatment for most diagnosed cases. CONCLUSION: The current project demonstrated the feasibility of applying rapid and upfront Xpert testing for presumptive infant TB cases. Rapid TB diagnosis in turn facilitates prompt and appropriate treatment initiation. Further, levels of rifampicin resistance observed in infants TB cases highlight the additional benefit of upfront resistance testing. However, high rates of early case mortality, in spite of prompt diagnosis and treatment initiation, highlight the need for further research in infant patient pathways for overall improvement in TB care for infant populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".