Bibliographic record
Abstract
PURPOSE: Rolandic epileptiform discharges with tangential dipole (T-dipole) configurations are associated with favorable prognosis. Whether the same is true for T-dipole epileptiform discharges in other brain regions is less established and is the objective of this study. METHODS: Over 20 years, patients with epileptiform discharges were identified as follows: frontal (F = 176), temporal (T = 196), central (C = 201), parietal (P = 120), and occipital (O = 205). T-dipoles were documented. Clinical features of children with and without T-dipole were compared both regardless of brain region and separately for each brain region. RESULTS: The prevalence of T-dipoles was 232/898 (25.8%) overall and within different regions as follows: T = 104 (53.1%), O = 51 (24.9%), P = 23 (19.2%), C = 35 (17.4%), and F = 19 (10.8%). Most had epilepsy (T-dipole: 193 [83.2%] and nondipole: 532 [79.9%]). Regardless of region, T-dipole was associated with less drug-resistant epilepsy (11 [4.7%] vs. 202 [30.3%], P < 0.001), developmental delay (57 [24.6%] vs. 436 [51.0%], P < 0.001), school performance difficulties (SPD) (101 [43.5%] vs. 410 [61.6%], P < 0.001), autism (30 [12.9%] vs. 127 [19.1%], P = 0.037), and abnormal examination (28 [12.1%] vs. 257 [38.6%], P < 0.001]). Within different brain regions, on logistic regression, T-dipole was associated with lower odds of drug-resistant epilepsy (F, T, C, P, and O), developmental delay (F, T, C, and P), SPD (F, T, and C), autism (F and T), abnormal examination (F, T, C, and O), and abnormal neuroimaging (T, C, P, and O). CONCLUSIONS: On routine EEG analysis, focal epileptiform discharges with T-dipoles, regardless of brain region, are associated with a more favorable clinical course.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".