Resolving ambiguities in SUDEP classification
Bibliographic record
Abstract
OBJECTIVE: To examine the consistency of applying the Nashef et al (2012) criteria to classify sudden unexpected death in epilepsy (SUDEP). METHODS: We reviewed cases from the North American SUDEP Registry (n = 250) and Medical Examiner Offices (n = 1301: 698 Maryland, 457 New York City, 146 San Diego). Two epileptologists with expertise in SUDEP and epilepsy-related mortality independently reviewed medical records, scene investigation, autopsy, and toxicology and assigned a SUDEP class. RESULTS: Major areas of disagreement arose between adjudicators concerned differentiating (1) Definite SUDEP Plus Comorbidity from Possible SUDEP and (2) Resuscitated (Near) SUDEP from SUDEP. In many cases, distinguishing between contributing and competing causes of death when trying to classify Definite SUDEP Plus Comorbidity versus Possible SUDEP is ambiguous and relies on judgement. Similarly, determining if an intervention was lifesaving or not (Resuscitated SUDEP or Not SUDEP), or if resuscitation merely delayed SUDEP (Resuscitated SUDEP or SUDEP) is often a judgement call and can differ between experienced adjudicators. Given these persisting ambiguities, we propose more explicit criteria for distinguishing these categories. SIGNIFICANCE: Accurate and consistent classification of cause of death among individuals with epilepsy remains a dire public health concern. SUDEP is likely underestimated in national health statistics. Greater standardization of criteria among epilepsy researchers, medical examiners, and epidemiologists to determine cause and classify death will lead to more accurate tracking of SUDEP and other epilepsy-related mortalities.
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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 | 0.251 | 0.547 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".