Screening of patients with diabetes mellitus for tuberculosis in <scp>I</scp> ndia
Bibliographic record
Abstract
OBJECTIVE: To assess the feasibility, results and challenges of screening patients with diabetes mellitus (DM) for tuberculosis (TB) within the healthcare setting of six DM clinics in tertiary hospitals across India. METHOD: Agreement on how to screen, monitor and record the screening was reached in October 2011 at a national stakeholders' meeting, and training was carried out for staff in the six tertiary care facilities in December 2011. Implementation started in the first quarter of 2012, and we report on activities up to 30th September 2012. Patients with DM were screened for TB on each clinic attendance using a symptom-based enquiry, and those with positive symptoms were referred for TB investigations. RESULTS: In the three quarters, 26% of 7218, 52% of 12237 and 48% of 11691 patients with DM were screened for TB. A total of 254 patients were identified with TB, of whom 46% had smear-positive pulmonary disease. There were 18 patients newly diagnosed with TB as a result of screening and referral, with the remainder being patients already diagnosed from elsewhere. TB case rates per 100,000 patients attending the DM clinic each quarter were 859, 956 and 642. Almost 90% of patients with TB were recorded as starting or being on anti-TB treatment. Major implementation challenges related to human resources and recording systems. CONCLUSION: In India, it is feasible to screen patients with DM for TB resulting in high rates of TB detection. More attention to detail, human resource requirements and electronic medical records are needed to improve performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".