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Record W2401090187 · doi:10.1016/s2214-109x(16)30045-6

Level of evidence of verbal autopsy – Authors' reply

2016· letter· en· W2401090187 on OpenAlexaff
Usha Ram, Rajesh Dikshit, Prabhat Jha

Bibliographic record

VenueThe Lancet Global Health · 2016
Typeletter
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Global Health ResearchSt. Michael's Hospital
Fundersnot available
KeywordsVerbal autopsyAutopsyMedicinePsychologyPathologyCause of death

Abstract

fetched live from OpenAlex

Most of the 60 million annual global deaths occur in low-income and middle-income countries and most of these occur without medical attention at the time of death, hence the causes of death are unknown.1 Thus, alternative systems are needed to measure causes of death especially to understand nationally representative patterns of deaths.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.395
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.044
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.395
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0040.003
Science and technology studies0.0020.003
Scholarly communication0.0060.007
Open science0.0060.004
Research integrity0.0250.015
Insufficient payload (model declined to judge)0.0440.019

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.

Opus teacher head0.306
GPT teacher head0.457
Teacher spread0.151 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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