Mixed impact of Xpert <sup>®</sup> MTB/RIF on tuberculosis diagnosis in Cambodia
Bibliographic record
Abstract
SETTING: National Tuberculosis (TB) Program sites in northwest Cambodia. OBJECTIVE: To evaluate the impact of Xpert(®) MTB/RIF at point of care (POC) as compared to non-POC sites on the diagnostic evaluation of people living with the human immunodeficiency virus (PLHIV) with TB symptoms and patients with possible multidrug-resistant (MDR) TB. DESIGN: Observational cohort of patients undergoing routine diagnostic evaluation for TB following the rollout of Xpert. RESULTS: Between October 2011 and June 2013, 431 of 822 (52%) PLHIV with TB symptoms and 240/493 (49%) patients with possible MDR-TB underwent Xpert. Xpert was more likely to be performed when available as POC. A smaller proportion of PLHIV at POC sites were diagnosed with TB than at non-POC sites; however, at POC sites, a higher proportion of those diagnosed with TB were bacteriologically positive. There was poor agreement between Xpert and other tests such as smear microscopy and culture. Overall, the evaluation of patients with possible MDR-TB increased following Xpert rollout, yet for patients confirmed as having drug resistance on drug susceptibility testing, only 46% had rifampin resistance that would be identified with Xpert. CONCLUSION: Although utilization of Xpert was low, it may have contributed to an increase in evaluations for possible MDR-TB and a decline in empiric treatment for PLHIV when available as POC.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".