Establishing a System for Medical Certification of Cause of Death for Noninstitutional Deaths in a Selected Area of Kolar District, Karnataka, India: Protocol for a Population-Based Feasibility and Validation Study
Bibliographic record
Abstract
BACKGROUND: Medical Certification of Cause of Death (MCCD) coverage in India is only 22.5%, largely due to a significant proportion of deaths occurring outside hospitals (noninstitutional deaths). The cause of death (CoD) in such cases is unlikely to be certified by any doctor. This study attempts to address this gap by developing an MCCD system for noninstitutional deaths in India. OBJECTIVE: This study will assess the feasibility of a physician-derived cause of death (PhyCoD) approach for deducing CoD in noninstitutional deaths in a selected area of Kolar Taluk, Karnataka, and validate this approach. METHODS: This population-based feasibility and validation study will be conducted in 4 selected hospitals and 2 Primary Health Centre (PHC) areas in Kolar taluk, Kolar district, Karnataka, India. We developed 4 PhyCoD questionnaires: maternal, neonatal, child, and adult. Institutional deaths that occurred over the previous 10 months in these selected hospitals with detailed case records available were selected as "gold standard" cases. Trained investigators abstracted the history from these case records into the questionnaires and deduced the CoD sequence of events. The investigators then elicited the history from the deceased's relatives using the PhyCoD questionnaire and deduced the CoD sequence of events. This will be compared with the gold standard CoD sequence of events deduced from medical records. The extent of agreement will be measured. The tool will be revised based on the pilot phase experiences. For all brought dead cases to the 4 hospitals and home deaths in the 2 PHC areas over a 3-month period, doctors in these hospitals and PHC medical officers, respectively, will elicit the history from the deceased's kin using the PhyCoD questionnaires and arrive at a CoD sequence of events. This CoD sequence of events will be validated against the gold standard autopsy whenever possible (in brought dead cases). The PhyCoD approach will also be tested for inter-rater reliability by independent investigators on a random sample of noninstitutional deaths. RESULTS: Institutional ethics committee clearance (January 2024), recruitment and training of project staff (January 2024-January 2025), preparation of questionnaires and application (August 2024-February 2025), pilot phase data collection (48 cases; August 2024-December 2024), and training of the doctors in the participating hospitals and PHC medical officers (December 2024) are complete. A total of 48 cases (32 adult, 7 child, 3 maternal, and 6 neonatal) were included in the pilot phase. Data review and analysis of the pilot phase data are underway. CONCLUSIONS: The study is expected to provide information about the validity and feasibility of the PhyCoD approach. Depending on the study's outcomes, the tool may be adopted by more states, leading to increased coverage of noninstitutional deaths under MCCD, improved accuracy, and reduced delay of CoD reporting for noninstitutional deaths. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72330.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".