Bibliographic record
Abstract
Seizures are a common manifestation of brain injury from diverse causes. In the intensive care unit (ICU), seizures present a particular diagnostic challenge: among critically ill patients, seizures frequently manifest with only subtle signs, or are entirely nonconvulsive (subclinical), without any clinical manifestations. Clinical assessment may be further clouded by the use of sedative medications or neuromuscular blocking agents. Seizures in this setting may be detectable only by EEG. Identification of nonconvulsive seizures is important because there is reason to believe that they may worsen brain injury, and therefore warrant treatment.1,–,4 In the current issue of Neurology ®, Abend and colleagues5 describe a series of critically ill children with altered mental status who underwent continuous EEG (cEEG) monitoring according to locally established clinical practice guidelines. They observed that 46 out of 100 children experienced seizures, of whom 70% had exclusively nonconvulsive seizures and 30% had both convulsive and nonconvulsive seizures. All children who experienced seizures had at least some nonconvulsive seizures. These observations substantiate the findings of several prior retrospective studies of critically ill infants and children that reported nonconvulsive seizure rates of …
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".