PW077 Are higher levels of C reactive protein predictive of positive echocardiographic findings of infective endocarditis ?
Bibliographic record
Abstract
Burden of tuberculosis in India remains enormous. The Revised National Tuberculosis Control Programme (RNTCP), based on the Directly Observed Treatment Short course (DOTS) strategy, was launched in 1997 in India. The question of what DOTS has or has not accomplished over the past 15 years is a central technical question.To assess the decadal impact of DOTS strategy on some epidemiological factors such as age and gender of new sputum positive (NSP) TB patients in Delhi.Secondary Data for Delhi was obtained from the state wise performance of RNTCP (Annual Summary) for the year 2001 and year 2012. Data was analyzed in Microsoft Excel 2007.The population of Delhi covered under DOTS has considerably increased over the decade. The case detection rate has also shown a considerable increase from 196/100,000 population in 2001 to 306/100,000 population at the end of Quarter 3 of 2011. The number of NSP male and female patients have increased in all age groups from 2001 to 2011 except in 25–34 years age group. NSP male patients on DOTS aged 15–44 years showed a left ward shift in increase, a significant right ward shift was noted in increase in female NSP patients of similar age group.The decadal assessment of DOTS in Delhi on TB epidemiology has pointed towards beginning of epidemiological transition in TB control in India.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".