International comparison of sudden unexpected death in infancy rates using a newly proposed set of cause-of-death codes
Bibliographic record
Abstract
BACKGROUND: Comparing rates of sudden unexpected death in infancy (SUDI) in different countries and over time is difficult, as these deaths are certified differently in different countries, and, even within the same jurisdiction, changes in this death certification process have occurred over time. AIMS: To identify if International Classification of Diseases-10 (ICD-10) codes are being applied differently in different countries, and to develop a more robust tool for international comparison of these types of deaths. METHODS: Usage of six ICD-10 codes, which code for the majority of SUDI, was compared for the years 2002-2010 in eight high-income countries. RESULTS: There was a great variability in how each country codes SUDI. For example, the proportion of SUDI coded as sudden infant death syndrome (R95) ranged from 32.6% in Japan to 72.5% in Germany. The proportion of deaths coded as accidental suffocation and strangulation in bed (W75) ranged from 1.1% in Germany to 31.7% in New Zealand. Japan was the only country to consistently use the R96 code, with 44.8% of SUDI attributed to that code. The lowest, overall, SUDI rate was seen in the Netherlands (0.19/1000 live births (LB)), and the highest in New Zealand (1.00/1000 LB). SUDI accounted for one-third to half of postneonatal mortality in 2002-2010 for all of the countries except for the Netherlands. CONCLUSIONS: The proposed set of ICD-10 codes encompasses the codes used in different countries for most SUDI cases. Use of these codes will allow for better international comparisons and tracking of trends over time.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".