PERFORMANCE OF REVISED NATIONAL TUBERCULOSIS CONTROL PROGRAMME IN RAJASTHAN AND TAMIL NADU DURING 2ND QUARTER 2001 TO 1ST QUARTER 2002
Bibliographic record
Abstract
India accounts for nearly one third of the global tuberculosis (TB) burden. Every day more than 20000 people are getting infected and out of these 5000 develop TB and more than 1000 people die of TB. More than 80% of the TB patients are in the economically productive age group of 15-54 years. National Tuberculosis Institute (NTI) formulated National Tuberculosis Control Programme in 1962 on a 50:50 sharing basis between center and state. The objectives of the programme were to reduce the morbidity & mortality to reduce disease transmission and to diagnose as many cases of TB as possible and to provide free treatment nearer to the TB patients home. However it could not make much of an impact on this disease. According to findings of programme review report - 1992 (Government of India (GOI) & World Health Organisation (WHO) the GOI evolved the revised strategy in the forms of Directly Observed Treatment Short course (DOTS) with the objective of curing at least 85% of the smear positive patients and detecting at least 70% of them. DOTS known as the Revised National Tuberculosis Control Programme (RNTCP) in India is a comprehensive strategy for TB Control. DOTS strategy has five components. It includes sustained government commitment effective laboratory based diagnosis standard treatment given under direct observation secured drug supply & systematic monitoring and evaluation. (excerpt)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".