A.06 Assessing inter-rater reliability in localizing sleep-related hypermotor seizures: a video-based survey
Bibliographic record
Abstract
Background: Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy characterized by abrupt sleep-related hypermotor seizures (SRHS) with complex semiology. Although difficult to localize within the frontal lobe recent studies using intracerebral EEG recordings have suggested the existence of four distinct semiology patterns (SP) organized in a rostro-caudal manner. It remains unclear however if these SP are clinically useful. Methods: We aimed to estimate the inter-rater reliability (IR) of classifying SP in SHE amongst epilepsy and sleep medicine experts. Following a short training session, ten experts were asked to review and classify 40 videos of SRHS in patients with confirmed SHE. IR was calculated using Kappa statistics. Results: SP1 and SP4, who are at the opposite ends of the SHE semiology spectrum, had substantial IR (0.82 and 0.67, respectively). Meanwhile, SP2 and SP3 showed fair agreement (0.25 and 0.35, respectively) and represented the major source of variance, with a small difference favouring epilepsy experts. Conclusions: Amongst epilepsy and sleep medicine experts, IR of classifying SRHS into four SP was only mildly satisfactory. SP1 and SP4 were shown to be easily recognizable while SP2 and SP3 were frequently confounded. Improvements in SP recognition are needed before widespread clinical use.
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 | 0.054 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".