The Iscore Predicts Total Healthcare Costs Early after Hospitalization for an Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: The ischemic Stroke risk score is a validated prognostic score which can be used by clinicians to estimate patient outcomes after the occurrence of an acute ischemic stroke. AIM: In this study, we examined the association between the ischemic Stroke risk score and patients' 30-day, one-year, and two-year healthcare costs from the perspective of a third party healthcare payer. METHODS: Patients who had an acute ischemic stroke were identified from the Registry of Canadian Stroke Network. The 30-day ischemic Stroke risk score prognostic score was determined for each patient. Direct healthcare costs at each time point were determined using administrative databases in the province of Ontario. Unadjusted mean and the impact of a 10-point increase ischemic Stroke risk score and a patient's risk of death or disability on total cost were determined. RESULTS: There were 12,686 patients eligible for the study. Total unadjusted mean costs were greatest among patients at high risk. When adjusting for patient characteristics, a 10-point increase in the ischemic Stroke risk score was associated with 8%, 7%, and 4% increase in total costs at 30 days, one-year, and two-years. The same increase was found to impact patients at low, medium, and high risk differently. When adjusting for patient characteristics, patients in the high-risk group had the highest total costs at 30 days, while patients at medium risk had the highest costs at both one and two-years. CONCLUSIONS: The ischemic Stroke risk score can be useful as a predictor of healthcare utilization and costs early after hospitalization for an acute ischemic stroke.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".