30th International Epilepsy Congress, Montreal, Canada, 23–27 June, 2013
Bibliographic record
Abstract
Purpose: In a population-based retrospective cohort of children with newly-diagnosed epilepsy, to ( 1) determine what proportion meet criteria for early medical intractability, and (2) predictors of enduring intractability.Methods: Children with newly-diagnosed epilepsy between 1980 and 2009 while resident in Olmsted County, MN, and followed >36 months were stratified into groups, based on both early medical intractability ('apparent' medical intractability in the first 2 years) and enduring intractability (persisting intractability at final follow-up or having undergone surgery for intractable epilepsy), and variables predicting these outcomes were evaluated.Results: Three hundred and eighty one children were included, representing 81% of our cohort with newly diagnosed epilepsy.Seventy five (19.7%) had early medical intractability, and predictors of this outcome on multivariable analysis were neuroimaging abnormality (p = 0.001), abnormal neurological examination at diagnosis (p = 0.005) and mode of onset [association was significant for focal vs generalized onset (p < 0.001) but not unknown vs generalized onset (p = 0.67)].After a median follow-up of 11.7 years, 49% remained intractable, 8% had rare seizures (≤q6 months), and the remainder were seizure-free.The only factor predicting for enduring intractability on multivariable analysis was neuroimaging abnormality (p < 0.0001).Conclusion: While a significant minority of children with early medical intractability ultimately achieved seizure control without surgery, those with an abnormal imaging study did poorly.For this subgroup, early surgical intervention is strongly advised to limit co-morbidities of ongoing, intractable seizures.Conversely, a cautious approach is suggested for those with normal imaging, as most will remit with 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.200 | 0.060 |
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".