Barreras de acceso a la cirugía de la epilepsia: revisión de la bibliografía
Bibliographic record
Abstract
Drug-resistant epilepsy, a chronic condition with long-term consequences can be treated with surgery. The efficacy and safety of surgery for temporal lobe epilepsy have been established through a large number of retrospective and prospective cohort studies and two randomized controlled clinical trials. Despite the excellent outcomes reported after surgery, the literature suggests that this procedure is an underutilized treatment. While evidence is lacking as to why epilepsy surgery is underused, cited reasons include: failure of primary care physicians and neurologists to provide information and identify patients who could be referred for surgery; different levels of technology at various centers, resulting in different candidate selection strategies; the belief that epilepsy surgery is a risky procedure and that it should be only viewed as the last option; patient preference to avoid surgery; parents wanting to wait until their child is old enough to participate in the decision-making process regarding surgery; unwillingness of insurers to cover the expenses associated with presurgical evaluations or lack of insurance; racial and social disparities, among others. In this paper we review the available epidemiological data about lack of utilization of epilepsy surgery.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.036 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".