<scp>ICD</scp> coding for epilepsy: Past, present, and future—A report by the International League Against Epilepsy Task Force on <scp>ICD</scp> codes in epilepsy
Bibliographic record
Abstract
The World Health Organization (WHO) International Classification of Diseases (ICD) has been used to classify causes of morbidity and mortality such as epilepsy for more than 50 years. The aims of this critical commentary are to do the following: (1) Introduce the ICD classification, summarize the ICD-9 and ICD-10 codes for epilepsy and seizures, and discuss the challenges of mapping epilepsy codes between these two versions; (2) discuss how the ICD-9 and ICD-10 relate to the revised International League Against Epilepsy (ILAE) terminology and concepts for classification of seizures and epilepsies; (3) discuss how ICD-coded data have been used for epilepsy care and research and briefly examine the potential impact of the international ICD-10 clinical modifications on research; (4) discuss the upcoming ICD-11 codes and the role of the epilepsy community in their development; and (5) discuss how the ICD-11 will conform more closely to the current ILAE terminology and classification of the epilepsies and seizures and its potential impact on clinical care, surveillance, and public health and research.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".