Analysis of tuberculosis case report in Hyderabad district of Telangana state
Bibliographic record
Abstract
BACKGROUND: The estimated incidence of TB in India was approximately 28,00,000 as per the Global TB report 2017. This accounts for a quarter of the world's TB (Tb) cases. National strategic plan, is a programme which aims at the elimination of Tb by 2018. The programme is crafted in line with other health sector strategies and global efforts, such as the draft National Health Policy 2015, World Health Organization's (WHO) End TB Strategy and the Sustainable Development Goals (SDGs) of the United Nations (UN). Key strategies under National Strategic plan: Private sector engagement, active case finding, drug resistant Tb case management, addressing social determinants including nutrition, robust surveillance system, community engagement and multisectoral approach. METHODS: In March 2018, India Tb report was released by RNTCP. An analysis of the report is done in the research article, an attempt to take forward end Tb strategy. RESULT: According to The India Tb report, 85% of new TB cases were detected, nationwide, where as 90% of new cases were detected in Hyderabad, during the same time period. Tuberculosis (Tb) notification rate (per 100 000 population), in India is 138, where as in Hyderabad district of Telengana it is 100. Both Public and private sector Tb case notification rate, of Hyderabad district was less than that of India (90,10). 6% of Tb cases were paediatric cases both Hyderabad and Nationwide. HIV status was known in 66% cases, in India and 67% in Hyderabad district of Telengana.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".