Bibliographic record
Abstract
Electrocorticography (ECOG), the intra-operative recording of cortical potentials, has played an important role in the surgical management of patients with medically refractory epilepsy. It has been used 1) to localize the epileptogenic tissue; 2) map out cortical functions; and 3) predict the success of the surgery. Despite its common use, few studies have been done to prove its effectiveness in these areas. The technique used in children for recording ECOG is very similar to that used in adults except for the limitations imposed by the child's age. Anaesthesia must often be used. Based upon a computerized medical literature search, a review of this procedure was done. Pre-resection localization, and post-resection prediction of outcome was done for temporal and extra-temporal resection, both lesional and nonlesional. Most of the available studies were in adult patients. All were retrospective in nature. Evidence for the role of pre-resection ECOG in determining the degree of resection felt necessary to obtain good clinical outcome was limited. Similarly the post-resection ECOG predication of surgical outcome was restricted.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.018 |
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".