Level 4 seizure monitoring unit admissions are associated with reduced long‐term health care costs
Bibliographic record
Abstract
OBJECTIVE: This study was undertaken to determine whether admission to dedicated seizure monitoring units (SMUs) result in reduced health care use (HCU). METHODS: This was a retrospective open cohort study covering the years 2010-2018 of patients residing in Alberta, Canada, who were referred to the Calgary Comprehensive Epilepsy Program and admitted to a level 4 SMU. Patients were required to have ≥3 years pre- and postadmission follow-up. The outcome was the change in trajectory of composite HCU (primary care, specialist outpatient visits, emergency department visits, and hospitalizations) for the 3 years prior to and 3 years following SMU admission using the point of admission as the "index date." Secondary outcomes were HCU limited to specific settings. We excluded the first 30 days following the point of admission to mitigate the confounding admission would have on the postadmission HCU trajectory. We used adjusted restricted maximum likelihood linear and nonlinear effects models to determine trajectories expressed as Canadian dollars. RESULTS: A total of 315 of 600 (53%) patients met eligibility criteria. Mean age was 40 years (SD = 17.4), 176 (56%) were female, 220 (70%) had focal epilepsy, and 60 (19%) had functional seizures or physiologic seizure mimics without epilepsy as adjudicated by the attending physician at the point of discharge. Mean per person health care costs increased by CAD$341.28 (95% confidence interval [CI] = -25.17 to 707.74) for each successive 6-month interval prior to SMU admission (p = .07). Following admission, mean per person costs decreased by CAD$802.34 (95% CI = 699.62-905.06, p < .001) for each successive 6-month interval up to 3 years postdischarge. Similar trends were noted for primary and specialist care, emergency department, admitted care, and when nonlinear models were applied. SIGNIFICANCE: Admission to an SMU is associated with significant and enduring declines in HCU. Each 6-months following discharge overall HCU declined by a mean of CAD$802.34 and acute inpatient, emergency department, and outpatient physician interactions declined by 25%, 26%, and 18% respectively. Comprehensive epilepsy care not only reduces morbidity and mortality but also reduces cost.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".