Burden of COPD among population above 30 years in India: protocol for a systematic review and proposed meta-analysis
Bibliographic record
Abstract
Background: The Sustainable Development Goals and the National Health Policy of India aim to reduce premature mortality from noncommunicable diseases (NCD) by one-third in the next decade and by 25% by 2025, respectively. Among NCDs globally, chronic obstructive pulmonary disease (COPD) is a major contributor to death and disability. This underscores the need to understand the burden of COPD at the national level by synthesizing evidence and collating the state-wise COPD data to estimate the prevalence of COPD and to highlight the associated risk factors to inform policymakers. Method: The systematic literature search will be carried out in PubMed, Cochrane, Scopus, Web of Science, CINAHL, and ProQuest databases with restrictions for studies published between 2000 and 2020 and available in English. Cross-sectional or cohort studies conducted in and among the Indian population aged 30 years and above will be included. Case reports, randomized trials, meta-analysis, commentaries, and qualitative studies will be excluded from the review. Quality assessment of the included studies will be performed using New Castle Ottawa scale and adherence to reporting standards will be checked using STROBE checklist for Observational Cohort and Cross-Sectional Studies. Discussion: Prevalence of COPD in the population aged 30 years and above, diagnosed through spirometry and nonspirometry, will be compared and reported and a meta-analysis will be performed to obtain pooled prevalence rates of COPD and the risk factors associated with COPD.
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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 | 0.081 | 0.097 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.066 | 0.007 |
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".