Electroencephalogram patterns in critical care: A primer for acute care doctors
Bibliographic record
Abstract
Electroencephalograms are commonly ordered by acute care doctors but not always understood. Other reviews have covered when and how to perform electroencephalograms. This primer has a different, unique, and complementary goal. We review basic electroencephalogram interpretation and terminology for nonexperts. Our goal is to encourage common understanding, facilitate inter specialty collaboration, dispel common misunderstandings, and inform the current and future use of this precious resource. This primer is categorically not to replace the expert neurologist or technician. Quite the contrary, it should help explain how nuanced electroencephalogram can be, and why indiscriminate electroencephalogram is inappropriate. Some might argue not to teach nonexperts lest they overestimate their abilities or reach. We humbly submit that it is even more inappropriate to not know the basics of a test that is ordered frequently and resource intensive. We cover the characteristics of the "normal" electroencephalogram, electroencephalogram slowing, periodic epileptiform discharges (and its subtypes), burst suppression, and electrographic seizures (and its subtypes). Alongside characteristic electroencephalogram findings, we provide clinical pearls. These should further explain what the reporter is communicating and whether additional testing is beneficial. Along with teaching the basics and whetting the appetite of the general clinician, this resource could increase mutual understanding and mutual appreciation between those who order electroencephalograms and those who interpret them. While there is more to electroencephalogram than can be delivered via a single concise primer, it offers a multidisciplinary starting point for those interested in the present and future of this commonly ordered test.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".