Database Systems and Online Tools for Early Referral to Epilepsy Surgery Evaluation in Mesial Temporal Lobe Epilepsy (P7.283)
Bibliographic record
Abstract
OBJECTIVE: Identify candidates to epilepsy surgery evaluation using an electronic database and the Canadian Appropriateness Study of Epilepsy Surgery (CASES) score. BACKGROUND: Mesial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis or gliosis, is the most common focal intractable epilepsy, and there is evidence of benefit for surgery in medically refractory cases. Recently, a study group developed an online tool to determine appropriateness for an epilepsy surgery evaluation, which is very important because of the reported delay for evaluation up to 18 years. DESIGN/METHODS: Since May 2th 2013 we assessed consecutive patients in our epilepsy clinic with current or suspected diagnosis of MTLE. RESULTS: We identified 32 cases of MTLE, 20 were women, and the mean patient age was 22 years (range 16-70 years); disease duration was 13 years (range 0-62). 17 were not seizure free and 15 were seizure free (>3 months). Among seizure free patients 13 self-reported adherence to treatment, 4 of them were on politherapy and one without treatment. Among patients currently having seizures, 9 were on monotherapy, 6 in 2-3 antiepileptic drus (AED) regime, and 2 with 蠅4 AEDs. 3 of them self-reported lack of adherence to treatment (2 in politherapy and one in 2-3 AEDs). 4 patients present >5 seizures/month, 6 patients 2-4 seizures/month y 5 patients 1 seizure/month. CASES score was calculated in 1 (12%), 7 (18%), 8 (23%) y 9 (47%) in patients currently having seizures, and scores 1 and 2 in seizure free patients. CONCLUSIONS: Our referral delay is similar to the previously reported, we identify 15 patients suitable for epilepsy surgery evaluation using the CASES score. The use of database systems and online tools is useful in the identification and early referral of patients with MTLE.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.016 |
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".