P.001 The use of evidence based guidelines to identify candidates for epilepsy surgery referral in a paediatric epilepsy practice
Bibliographic record
Abstract
Background: Approximately 95,000 Ontarians, including nearly 15,000 children, have a diagnosis of epilepsy (CCSO, 2015). Management is typically with medication, though surgical resection offers permanent cure in a subsection of patients. A 2012 Health Quality Ontario study estimated a potential 9,300 patients as surgical candidates, but only 150 operations are performed annually, suggesting the surgery option is underutilized. This study attempts to identify reasons for non-referral in a small epilepsy practice. Methods: Evidence based guidelines (Jette, N et al, CMAJ 2014) were used to define eligibility for surgical referral in 274 children with epilepsy. The presence of referral criteria was analyzed. Results: 22 children had clear evidence of drug resistance and one other criteria for referral for epilepsy surgery. 10 referrals had been made. Complex syndromes were present in 40% of the referrals, and 10% of the unreferred. Surgical lesions were present on MRI in 60% of the referrals and 14% of the unreferred. The majority of the unreferred have refractory focal epilepsy but no known surgical abnormality on MRI. Conclusions: In our practice there is a referral bias towards patients with MRI lesions, whereas those without MRI findings tend not to be referred, despite being refractory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".