The Accuracy of Outcome Prediction Models for Childhood‐onset Epilepsy
Bibliographic record
Abstract
PURPOSE: Two large prospective cohort studies of childhood epilepsy (Nova Scotia and the Netherlands) each developed a statistical model to predict long-term outcome. We sought to evaluate the accuracy of a prognostic model based on the two studies combined. METHODS: Analyses with classification tree models and stepwise logistic regression produced predictive models for the combined dataset and the two separate cohorts. The resulting models were then externally validated on the opposite cohort. Remission was defined as no longer receiving daily medication for any length of time at the end of follow-up. RESULTS: The combined cohorts yielded 1,055 evaluable patients. At the end of follow-up (>or=5 years in >96%), 622 (59%) were in remission. By using the combined data, the classification tree model and the logistic regression model predicted the outcome correctly in approximately 70%. The classification tree model split the data on epilepsy type and age at first seizure. Predictors in the logistic regression model were: seizure number before treatment, age at first seizure, absence seizures, epilepsy types of symptomatic generalized and symptomatic partial, preexisting neurologic signs, intelligence, and the combination of febrile seizures and cryptogenic partial epilepsy. When the prediction models from each cohort were cross-validated on the opposite cohort, the outcome was predicted slightly less accurately than did the model from the combined data. CONCLUSIONS: Based on currently available clinical and EEG variables, predicting the outcome of childhood epilepsy may be difficult and appears to be incorrect in about one of every three patients.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".