Delayed diagnosis in adolescent onset focal epilepsy: Impact on morbidity and mental health
Bibliographic record
Abstract
Abstract Objective This study was undertaken to investigate diagnostic delay in adolescent onset focal epilepsy, including reasons for longer delays and associated morbidities. Methods Secondary analysis was done using enrollment data from the Human Epilepsy Project, a multi‐institutional cohort including 34 sites in the USA, Canada, Finland, Austria, and Australia (2012–2017). Participants were aged 11–64 years at enrollment and within 4 months of treatment initiation for newly diagnosed focal epilepsy. Participants with seizure onset at age ≤ 21 years were evaluated. Data included seizure diaries documenting onset, frequency, and characteristics of seizures, reasons for diagnostic delays, and prediagnosis morbidities, including injuries, suicidal ideation, and self‐injurious behaviors. Results Of 152 participants with adolescent onset seizures, those with a diagnosis delay > 1 year experienced a median delay to diagnosis of 4.4 years and reported higher rates of initial nonmotor seizures compared to those diagnosed within 1 year ( n = 55, 78.6% vs. n = 33, 40.2%; χ 2 1 = 22.76, p < .001). Lack of recognition by patients and health care providers accounted for diagnostic delay in more than half (68.2%) of participants with initial nonmotor seizures. Notably, 70% of participants with initial nonmotor seizures went undiagnosed until development of motor seizures. This group reported more injuries compared to those who did not develop motor seizures (56.5% vs. 7.7%; χ 2 2 = 19.82, p < .001). Those with time to diagnosis > 1 year had more prediagnostic seizures (67 vs. 5.5 seizures, 95% confidence interval = 16.0–94.0; p < .001) and higher rates of suicidal ideation (33.3% vs. 14.9%; χ 2 1 = 6.27, p = .01), suicidal behaviors (13.8% vs. 1.5%; χ 2 1 = 7.19, p = .007), and nonsuicidal self‐injurious behaviors (13.4% vs. 1.5%; χ 2 1 = 7.04, p = .008). Significance This study highlights significant delays in diagnosing adolescent onset focal epilepsy, especially in cases with nonmotor seizures. These delays, often due to lack of recognition by patients and health care providers, are linked to more frequent seizures, higher injury rates, and increased suicidal ideation and self‐injury. Early recognition and diagnosis may mitigate adverse outcomes and improve quality of life for adolescents with epilepsy.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".