P.10 Adding a neuroimaging safety net to the work up of status epilepticus at the Ottawa Hospital
Bibliographic record
Abstract
Background: CT angiogram of the head and neck (CTA) is not part of the routine work-up of status epilepticus (SE), which could miss acute ischemic stroke (AIS) as the cause. We hypothesized that healthcare savings from early treatment of otherwise undiagnosed AIS would be greater than the cost of adding a routine CTA for work-up of SE (all comers). Methods: The total number of patients presenting to ER with SE (defined as seizure/epilepsy+hospital admission), and the subgroup who were diagnosed with a new ischemic stroke, or received a CTA were retrospectively calculated at the Ottawa Hospital between 2010-2019. CTA costs, and savings of early treatment of AIS were obtained from the Department of Radiology and literature review, respectively. Results: 727 individuals presented with SE. 3% (n=22) had a new ischemic stroke-of these, 95% (n=21) did not receive a CTA (considered missed AIS). Assuming CTA could help detect every case of ischemic stroke missed this could result in 2.27 additional strokes caught early/year, and assuming if all thrombolysis candidates this would net cost $7,967/year (vs no acute treatment), or if all thrombolysis+thrombectomy candidates would net save $19,823/year (vs thrombolysis alone). Conclusions: Routine CTA in SE in the ER has potential to result in healthcare savings.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.007 |
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".