EEG Telemetry with Closely Spaced Electrodes in Frontal Lobe Epilepsy
Bibliographic record
Abstract
The use of additional electrodes (other than standard 10-20 electrodes) has proved to be extremely useful in the investigation of patients with temporal lobe epilepsy. The development of 32- and 64-channel EEG machines, along with the reformatting capabilities of digital EEG has greatly increased the possibilities in the number of electrodes and recording montages. The authors wanted to determine whether the use of closely spaced electrodes designed to increase the coverage of frontocentral regions is of benefit in the investigation of patients with frontocentral epilepsy. Patients investigated for frontocentral epilepsy underwent EEG telemetry with closely spaced electrodes based on the 10-10 nomenclature. Twenty-three patients were studied. An additional 30 minutes was required by technicians to create the montage. Unilateral frontal or frontocentral epileptic abnormalities were observed in 10 patients, independent bifrontal in 5 patients, synchronous bifrontal in 4 patients, and no EEG changes in 4 patients. In no patient did the addition of closely spaced electrodes lead to a change in the classification of the EEG. Closely spaced electrodes did not reveal focal abnormalities, which were not already apparent with 10-20 electrodes, nor did they demonstrate evidence of laterality in bilaterally synchronous discharges.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".