Recommendations Regarding the Requirements and Applications for Long‐term Recordings in Epilepsy
Bibliographic record
Abstract
The purpose of this paper is to update the state of knowledge with respect to long-term monitoring (LTM) in epilepsy and to formulate recommendations regarding the application of LTM in clinical practice. LTM is an established technique in use both in a hospital setting and, increasingly, in an ambulatory and more recently in a community-based setting. There has been sufficient evidence to substantiate the claim that LTM is of crucial importance in documenting electroclinical correlations both in epilepsy and in paroxysmally occurring behavioral changes often mistaken for epilepsy. Internationally recognized neurophysiological equipment standards, data acquisition and data transfer protocols and widely accepted safety standards have made widespread access to LTM facilities in epilepsy possible. Recommendations on efficient and effective use of resources as well as regarding training and competencies for personnel involved in LTM in epilepsy have been formulated. The DMC Neurophysiology Subcommittee of the ILAE recommends use of hospital-based LTM in the documentation of seizures including its application for assessing seizure type and frequency, in the evaluation of status epilepticus, in noninvasive and invasive video/EEG investigations for epilepsy surgery and for the differential diagnosis between epilepsy and paroxysmally occurring nonepileptic conditions, in children and in adults. Ambulatory outpatient and community-based LTM may be used as a substitute for inpatient LTM in cases where the latter is not cost-effective or feasible or when activation procedures aimed at increasing seizure yield are not indicated. However, outpatient ambulatory monitoring may be less informative than is inpatient monitoring in some cases because: (1) reduction of medication to provoke seizures may not be safe as an outpatient; (2) faulty electrode contacts cannot quickly be noticed and repaired; (3) the patient may move out of video surveillance; and (4) duration of ambulatory monitoring can be limited by technical constraints.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".