Heart Rate Variability in Insulo-Opercular Epilepsy
Bibliographic record
Abstract
Background: We aimed to evaluate heart rate variability (HRV) changes in insulo-opercular epilepsy (IOE) and after insulo-opercular surgery. Methods: We analyzed 5-min resting HRV of IOE patients before and after surgery. Patients’ SUDEP-7 risk inventory scores were also calculated. Results were compared with age- and sex-matched patients with temporal lobe epilepsy (TLE) and healthy individuals. Results: There were no differences in HRV measurements between IOE, TLE, and healthy control groups (and within each IOE group and TLE group) in preoperative and postoperative periods. In IOE patients, the SUDEP-7 score was positively correlated with pNN50 (percentage of successive RR intervals that differ by more than 50 ms) (p = 0.008) and RMSSD (root mean square of successive RR interval differences) (p = 0.019). We stratified IOE patients into those whose preoperative RMSSD values were below (Group 1a = 7) versus above (Group 1b = 9) a cut-off threshold of 31 ms (median value of a healthy population from a previous study). In group 1a, all HRV values significantly increased after surgery. In group 1b, time-domain parameters significantly decreased postoperatively. Conclusions: Our results suggest that in IOE, HRV may be either decreased in parasympathetic tone or increased globally in both sympathetic and parasympathetic tones. We found no evidence that insulo-opercular surgeries lead to major autonomic dysfunction when a good seizure outcome is reached. The increase in parasympathetic tone observed preoperatively may be of clinical concern, as it was positively correlated with the SUDEP-7 score.
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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 | 0.001 |
| 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) | 0.001 | 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".