Heterogeneous knowledge of childhood seizures and epilepsy care in Canadian healthcare Providers: Identifying the gaps
Bibliographic record
Abstract
Epilepsy is the most common chronic neurological condition in children. Many barriers exist in early recognition which cause delay in care and impact quality of life. Some of these children require advanced treatments which are underutilized due to lack of education, awareness and referrals. Overall, childhood epilepsy is underdiagnosed and poorly understood by non-expert providers. We investigated awareness and knowledge about epilepsy from primary care providers via the quality of their referrals. We prospectively collected and examined all epilepsy related referrals to the Paediatric Neurology Division at Children's Hospital in London, Ontario, Canada during a six-month period. We developed a modified "epilepsy focused" scoring tool to evaluate the referrals and scored them as basic or advanced. During the study time frame 175 (82 %) referrals met the inclusion criteria. Out of these, 152 (87 %) were identified as basic and 23 (13 %) were advanced (p < 0.001). Amongst the referrals that scored basic vs advanced: Family Doctors n = 49 with 40 basic (81 %) vs 9 advanced (18.3 %), Paediatric ER physicians n = 37, all 37 were basic (100 %) and Paediatricians n = 41 with 36 (87 %) basic and 5 (12 %) advanced. Our results showed significant lack of critical information in the content of epilepsy referrals coming from non-epileptologist providers, largely from the cohort of paediatric ED doctors. This reveals that knowledge and awareness of epilepsy in children remains scarce. Identifying these barriers can provide insights to develop strategies to facilitate accurate identification and rapid triage for children presenting with new onset 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".