Strategic and principled approach to the ethical challenges of epilepsy monitoring unit triage
Bibliographic record
Abstract
Electroencephalographic monitoring provides critical diagnostic and management information about patients with epilepsy and seizure mimics. Admission to an epilepsy monitoring unit (EMU) is the gold standard for such monitoring in major medical facilities worldwide. In many countries, access can be challenged by limited resources compared to need. Today, triaging admission to such units is generally approached by unwritten protocols that vary by institution. In the absence of explicit guidance, decisions can be ethically taxing and are easy to challenge. In an effort to address this gap, we propose a two-component approach to EMU triage that takes into account the unique landscape of epilepsy monitoring informed by triage literature from other areas of medicine. Through the strategic component, we focus on the EMU wait list management infrastructure at the institutional level. Through the principled component, we apply a combination of the ethical principles of prioritarianism, utilitarianism and justice to triage; and we use individual case examples to illustrate how they apply. The effective implementation of this approach to specific epilepsy centres will need to be customised to the nuances of different settings, including diverse practice patterns, patient populations and constraints on resource distribution, but the conceptual consolidation of its components can alleviate some of the pressures imposed by the complex decisions involved in EMU triage.
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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 | 0.010 | 0.012 |
| 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 | 0.001 | 0.007 |
| 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".