The Cost of After-Hour Electroencephalography
Bibliographic record
Abstract
Background and Objectives: High costs associated with after-hour electroencephalography (EEG) constitute a barrier for financially constrained hospitals to provide this neurodiagnostic procedure outside regular working hours. Our study aims to deepen our understanding of the cost elements involved in delivering EEG services during after-hours. Methods: We accessed publicly available data sets and created a cost model depending on 3 most commonly seen staffing scenarios: (1) technologist on-site, (2) technologist on-call from home, and (3) a hybrid of the two. Results: Cost of EEG depends on the volume of testing and the staffing plan. Within the various cost elements, labor cost of EEG technologists is the predominant expenditure, which varies across geographic regions and urban areas. Discussion: We provide a model to explain why access to EEGs during after-hours has a substantial expense. This model provides a cost calculator tool (made available as part of this publication in eAppendix 1, links.lww.com/CPJ/A513) to estimate the cost of EEG platform based on site-specific staffing scenarios and annual volume.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".