The impact of chest radiography and Xpert MTB/RIF testing among household contacts in Chennai, India
Bibliographic record
Abstract
Tuberculosis prevalence surveys have demonstrated the benefit of screening with chest x-ray (CXR) and sensitive diagnostic tests compared to symptoms and smear microscopy. However, in programmatic practice there is little evidence on the yield of different algorithms. We implemented contact tracing in Chennai, India for adult sputum-positive TB patients registered from January 2015 to March 2016. Patients with symptoms or abnormal X-ray findings further underwent testing using Xpert MTB/RIF (Xpert) and smear microscopy. A retrospective cohort study was done to summarize the key findings. We verbally screened 5553 contacts for symptoms, CXR through private sector collaboration, Xpert, and smear microscopy. Overall, 1312 (23.6%) contacts screened positive. CXR alone identified 531 (40.5%) of them, 679 (51.8%) were symptom-positive only, while 102 (7.8%) were positive on both the symptom and CXR screen. Overall, 35 bacteriologically positive cases were identified (0.7%). A standard approach of symptoms screening followed by microscopy identified only 9 (25.7%) of the total number of bacteriologically positive cases, whereas the combination of a CRX screening followed by microscopy identified 13 (37.1%) of the cases. The algorithm of symptoms screening followed by Xpert testing, detected 20 cases, whereas the combination of symptoms and CXR followed by Xpert increased this number to 35 (75% increase compared to symptoms and Xpert). Optimal use of more sensitive screening tests, better diagnostic tests, and novel private sector engagement can improve diagnostic yield in a programmatic setting.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".