Validation of verbal autopsy to determine the cause of 137 neonatal deaths in Karachi, Pakistan
Bibliographic record
Abstract
Verbal autopsy (VA) aims to estimate a community's mortality experience in the absence of contact with formal registration or health care systems. Application of VA to neonatal deaths is problematic as the agonal phase of a neonatal death tends to be indistinct. This is the first attempt to validate the technique exclusively on newborns who died. Seriously ill neonates (n = 137) were enrolled from the Civil Hospital, Karachi, Pakistan, between 31 October 1993 and 31 July 1994. All died as newborns, and caregivers were interviewed at home 3-230 days later. Surveillance physicians completed case questionnaires in the hospital, and investigator physicians assigned the main and associated causes of death using clinical criteria. Field questionnaires including a verbatim open-ended history, and syndrome modules were completed by a field worker, and investigator physicians again assigned the main and associated causes of death based on three diagnostic methods: verbatim alone, modules alone and verbatim and modules combined. We assessed the validity of VA by comparing field against hospital diagnoses by diagnostic (verbatim vs. modules vs. both) and analytic method (main vs. any diagnosis). VA identified at least one diagnosis accurately in 71% of the newborns. VA underdiagnosed low birthweight and prematurity in the field. Verbatim and modules diagnostic method comparing any field against main hospital diagnoses revealed high sensitivities for too early/too small syndrome (90%) and neonatal tetanus (84%). VA correctly identified some important causes of neonatal death in the field. Assigning multiple diagnoses using both open- and closed-ended questions increases the likelihood of correct ascertainment.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".