Examination of respiratory specimens improves microbiological diagnosis of patients with presumptive extrapulmonary tuberculosis
Bibliographic record
Abstract
OBJECTIVES: Bacteriological confirmation of extrapulmonary tuberculosis (EPTB) is challenging for several reasons: the paucibacillary nature of the sample; scarce resources, mainly in middle and low-income countries; the need for hospitalization; and unfavorable outcomes. We evaluated the diagnostic role of respiratory specimen examination prospectively in a cohort of patients with presumptive EPTB. METHODS: From July 2018 to January 2019, in a tuberculosis (TB)/HIV reference hospital, a cohort of 157 patients with presumed EPTB was evaluated. Xpert® MTB/RIF Ultra or a culture-positive result was considered for bacteriologically confirmed TB. RESULTS: Out of 157 patients with presumptive EPTB, 97 (62%) provided extrapulmonary and respiratory specimens and 60 (38%) extrapulmonary specimens only. Of the 60 patients with extrapulmonary samples, 5 (8%) were positive. Of those with respiratory and extrapulmonary samples, 27 (28%) were positive: 10 in both the respiratory and extrapulmonary samples, 6 in the extrapulmonary sample only, and 11 in the respiratory sample only. A respiratory specimen examination increased by 6-fold the chance of bacteriological confirmation of TB (odds ratio = 5.97 [1.11-47.17]). CONCLUSION: We conclude that respiratory samples should be examined in patients with presumptive EPTB.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".