Capturing the Context of Maternal Deaths from Verbal Autopsies: A Reliability Study of the Maternal Data Extraction Tool (M-DET)
Bibliographic record
Abstract
Background The availability of quality data to inform policy is essential to reduce maternal deaths. To characterize maternal deaths in settings without complete vital registration systems, we designed and assessed the inter-rater reliability of a tool to systematically extract data and characterize the events that precede a nationally representative sample of maternal deaths in India. Method/Principal Findings Of 1017 nationally representative pregnancy-related deaths, which occurred between 2001 and 2003, we randomly selected 105 reports. Two independent coders used the maternal data extraction tool (questions with coding guidelines) to collect information on antenatal care access, final pregnancy outcome; planned place of birth and care provider; community consultation, transport, admission, hospital referral; and verification of cause of death assignment. Kappa estimated inter-rater agreement was calculated and classified as poor (K≤0.4), moderate (K = 0.4-≤0.6), substantial (K = 0.6-≤0.8) and high (K>0.8) using the criteria from Landis & Koch. The data extraction tool had high agreement for gestational age, pregnancy outcome, transport, death en route and admission to hospital; substantial agreement for receipt of antenatal care, planned place of birth, readmission and referral to higher level hospital, and whether or not death occurred in the intrapartum period; moderate to substantial agreement for classification of deaths as direct or indirect obstetric deaths or incidental deaths; moderate agreement for classification of community healthcare consultation and total number of healthcare contacts; and poor agreement for the classification of deaths as sudden deaths and other/unknown cause of death. The ability of the tool to identify the most-responsible-person in labour varied from moderate agreement to high agreement. Conclusions This data extraction tool achieved good inter-rater reliability and can be used to collect data on events surrounding maternal deaths and for verification/improvement of underlying cause of death.
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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 | 0.192 | 0.370 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".