Hyperventilation‐induced High‐amplitude Rhythmic Slowing with Altered Awareness: A Video‐EEG Comparison with Absence Seizures
Bibliographic record
Abstract
PURPOSE: Hyperventilation-induced high-amplitude rhythmic slowing (HIHARS) in children may be associated with clinical episodes of altered awareness. The presence of automatisms has been proposed as a distinguishing feature that helps to differentiate absence seizures from nonepileptic causes of decreased responsiveness. This retrospective, controlled, video-EEG study compared the clinical characteristics of episodes of HIHARS with loss of awareness with those of absence seizures. METHODS: The database of a tertiary Children's Hospital was searched for patients studied between April 1993 and April 1997 who had at least one episode of HIHARS with loss of awareness. The absence control group was obtained by selecting the next patient, after an HIHARS study subject, who met the following criteria: (a) had at least one absence seizure occurred during hyperventilation in the EEG recording, and (b) had a diagnosis of idiopathic generalized epilepsy. The video-EEG and medical histories of all patients were reviewed and summarized. RESULTS: We reviewed video-EEG recordings of 77 episodes of HIHARS with loss of awareness from 22 children and 107 absence seizures during hyperventilation from 22 children. Eye opening and eyelid flutter were seen more frequently in absence seizures, whereas fidgeting, smiling, and yawning occurred more frequently during HIHARS episodes. Arrest of activity, staring, and oral and manual automatisms were observed in both groups. CONCLUSIONS: Automatisms are common in both HIHARS and absence seizures. Yawning, smiling, and particularly fidgeting occur more commonly and eye opening and eyelid flutter less commonly in HIHARS. However, episodes of HIHARS with loss of awareness clinically mimic absence seizures, and these conditions can be distinguished reliably only by EEG.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".