Diagnostic accuracy of WHO verbal autopsy tool for ascertaining causes of neonatal deaths in the urban setting of Pakistan: a hospital-based prospective study
Bibliographic record
Abstract
BACKGROUND: Globally, clinical certification of the cause of neonatal death is not commonly available in developing countries. Under such circumstances it is imperative to use available WHO verbal autopsy tool to ascertain causes of death for strategic health planning in countries where resources are limited and the burden of neonatal death is high. The study explores the diagnostic accuracy of WHO revised verbal autopsy tool for ascertaining the causes of neonatal deaths against reference standard diagnosis obtained from standardized clinical and supportive hospital data. METHODS: All neonatal deaths were recruited between August 2006 -February 2008 from two tertiary teaching hospitals in Province Sindh, Pakistan. The reference standard cause of death was established by two senior pediatricians within 2 days of occurrence of death using the International Cause of Death coding system. For verbal autopsy, trained female community health worker interviewed mother or care taker of the deceased within 2-6 weeks of death using a modified WHO verbal autopsy tool. Cause of death was assigned by 2 trained pediatricians. The performance was assessed in terms of sensitivity and specificity. RESULTS: Out of 626 neonatal deaths, cause-specific mortality fractions for neonatal deaths were almost similar in both verbal autopsy and reference standard diagnosis. Sensitivity of verbal autopsy was more than 93% for diagnosing prematurity and 83.5% for birth asphyxia. However the verbal autopsy didn't have acceptable accuracy for diagnosing the congenital malformation 57%. The specificity for all five major causes of neonatal deaths was greater than 90%. CONCLUSION: The WHO revised verbal autopsy tool had reasonable validity in determining causes of neonatal deaths. The tool can be used in resource limited community-based settings where neonatal mortality rate is high and death certificates from hospitals are not available.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".