A comparison of waiting times for assessment and epilepsy surgery between a Canadian and a Mexican referral center
Bibliographic record
Abstract
Objective: To provide a comprehensive transnational overview of wait times for epilepsy surgery in Canada and Mexico. Methods: We reviewed all cases referred for epilepsy surgery between 2007 and 2015 at the Saskatchewan Epilepsy Program Royal University Hospital (SEP) (n = 70; Saskatoon, Canada) and the National Institute of Neurology and Neurosurgery (NINN) (n = 76; Mexico City, Mexico) and compared wait times, calculated as the time from diagnosis of epilepsy on assessment at an epilepsy center to epilepsy surgery. Results: Mean wait times were similar across centers. Mean patient age was 37.4 ± 9 years (NINN) and 36.7 ± 13.2 years (SEP). The mean time from epilepsy diagnosis to referral was 18.9 (NINN) and 16.9 years (SEP), p = 0.30; first consult with the epileptologist, 19.7 (NINN) and 17.4 years (p = 0.23); neuropsychology consult, 21.4 (NINN) and 17.9 years (SEP); video electroencephalogram (video-EEG) telemetry, 21.1 (NINN) and 18.6 months (SEP); initial neurosurgical consult, 21.9 (NINN) and 19.1 years (SEP) (p = 0.35); and epilepsy surgery, 19.7 (NINN) and 19.6 years (SEP) (p = 0.29). Significance: This is the first study to compare wait times between Canada and Mexico. Despite disparity in their health delivery systems and financial resources, surgical wait times appeared to be protracted in both nations, confirming that delayed treatment is a universal problem that requires collaborative scrutiny.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".