Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Using the most recent evidence, we provide an update on epilepsy surgery, focusing on its effectiveness, reasons for underutilization, considerations of candidacy and timing for referral for epilepsy surgery evaluation. RECENT FINDINGS: The course of illness of epilepsy is being characterized. Well conducted studies describe the patterns of seizure remission and relapse with medical therapy and also in response to epilepsy surgery. Epilepsy surgery is highly effective in selected patients with drug-resistant epilepsy (DRE). The risk-benefit of epilepsy surgery is well known and consistent around the world. However, epilepsy surgery remains underutilized. A randomized controlled trial and Clinical Practice Guidelines (CPGs) supporting epilepsy surgery have had no discernible impact on referral rates for epilepsy surgery evaluation. Criteria and guidelines are being developed for identifying patients who need to be referred for epilepsy surgery evaluation. Quality indicators for epilepsy care now also include the need to consider surgical candidacy every 3 years in DRE. New developments in imaging and neurophysiology promise to help clinicians identify and treat patients more accurately. SUMMARY: Surgery is effective but underused. Comprehensive interventions to translate evidence to practice in epilepsy surgery are urgently needed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it