Single Unprovoked Seizure: Wait Time to Full Medical Assessment, Does It Matter?
Bibliographic record
Abstract
Introduction Single unprovoked seizures occur in about 4% of the population and they have significant psychosocial consequences for the patients and their families. Little information is available on the timeliness and safety of assessment of first unprovoked seizures. In this study, we review the timeliness of the referral and evaluation of patients with first unprovoked seizure in a Canadian neurological provincial referral center. Method Retrospective analysis of 51 patients over a 3.5 year period was performed and data were collected on patient demographics, date of event and time to evaluation by the epileptologist, evaluations completed, treatments initiated and patient outcomes. Results We found that most patients were seen by the epileptologist within 6 months, there was only a 9% discrepancy in final diagnoses between the epileptologist and the referring physician, and there were no fatalities or serious complications in the patients we studied. However, a few patients waited very long periods before imaging and evaluation by the epileptologist, and restrictions on driving privileges were recommended in only 3% of the patients. Conclusions We conclude that the referral process for a first unprovoked seizure is timely. Primary care providers need further education with regards to the consequences of seizures and some areas of the referral region need better access to imaging and epileptologists.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".