Inconsistent classification of unexplained sudden deaths in infants and children hinders surveillance, prevention and research: recommendations from The 3rd International Congress on Sudden Infant and Child Death
Bibliographic record
Abstract
This report details the proceedings and conclusions from the 3rd International Congress on Unexplained Deaths in Infants and Children, held November 26-27, 2018 at the Radcliffe Institute at Harvard University. The Congress was motivated by the increasing rejection of the diagnosis Sudden Infant Death Syndrome (SIDS) in the medical examiner community, leading to falsely depressed reported SIDS rates and undermining the validity and reliability of the diagnosis, which remains a leading cause of infant and child mortality. We describe the diagnostic shift away from SIDS and the practical issues contributing to it. The Congress was attended by major figures and opinion leaders in this area from countries significantly engaged in this problem. Four categories (International Classification of Diseases (ICD)-11 categories of MH11, MH12, MH14, PB00-PB0Z) were recommended for classification, and explicit definitions and guidance were provided for death certifiers. SIDS was reframed as unexplained sudden death in infancy or SIDS/MH11 to emphasize that either term signifies the lack of explanation following a rigorous investigation. A distinct category for children over the age of 1 was recommended (MH12). Definitions and exclusions were provided for the alternative categories of accidental asphyxia and undetermined. As recommended, unexplained sudden death in infancy or SIDS on a death certificate will code a unique, trackable entity, accurately reflecting the inability to determine a definitive explanation, while satisfying surveillance needs and reliable identification for research efforts. The conclusions will be submitted to the World Health Organization for inclusion in the upcoming ICD-11.
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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 | 0.291 | 0.391 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.013 | 0.013 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".