Proportion of pulmonary tuberculosis cases diagnosed at different levels of health care across fourteen districts of Kerala
Bibliographic record
Abstract
Background: It is estimated that 10.4 million cases and 1.7 million deaths occur due to tuberculosis (TB) globally. More than one quarter of TB cases and TB-related deaths worldwide occur in India each year. Kerala's TB incidence is estimated to be 67 cases per 100,000. Objective was to estimate the proportion of Pulmonary TB cases diagnosed at different levels of the health care system across all the fourteen districts of Kerala from January to September 2019.Methods: A secondary data analysis was conducted on information obtained from the NIKSHAY portal from January to September 2019. Proportion of cases detected at PHC, CHC, THQ, District hospital and other tertiary care facilities was computed. Statistical analysis was performed using statistical package of social sciences (SPSS) version 23.0.Results: The maximum number of new TB cases (70.8%) was being detected at the primary care level, while 20.3% of new cases were detected from tertiary care centres and 8.9% from secondary care centres. At the primary healthcare level, the maximum number of newly diagnosed TB cases was reported from Wayanad district (88.0%) while, in the secondary and tertiary care levels, Kollam district was found to diagnose the maximum number of new TB cases (24.0% and 48.4% respectively).Conclusions: In this study, majority of the new TB cases were being diagnosed at the Primary health care level. However, in few districts the secondary and tertiary care centres were found to be diagnosing a greater number of cases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".