Abstract TP79: Utilization and Costs Associated With Diagnostic Imaging in Acute Stroke
Bibliographic record
Abstract
Introduction: Diagnosis and treatment of acute stroke depends upon imaging, namely non-contrast CT (NCCT), CT angiography (CTA), CT perfusion (CTP), and MRI. We sought to i) examine available data from around the world on six categories related to acute stroke imaging: utilization, time to imaging, radiation exposure, contrast dose, availability, and cost, and ii) compare that information with similar data from our province (Alberta). Methods: Using a scoping review methodology, six categories related to acute stroke diagnostic imaging were examined: utilization, time to imaging, radiation exposure, contrast dose, availability, and cost. Metadata for these categories in Alberta were collected from Alberta Health Services administrative records for comparison. Results: A total of 6693 articles/documents were initially identified through database searches, of which 83 were included for analysis. CT (range = 55-100%) was used more frequently than MRI (range = 23.6-94.0%) in acute stroke, with high CTA use in Alberta (55.7%) compared to other centres (range = 8.6-36.0%; Table 1). CT time to imaging was < 25 min (target time) in most centres, but not in Alberta (mean = 3.2 hr, median = 2.0 hr). MRI was not used urgently in most centres (mean = 0.4-21.0 hr), including Alberta (mean = 15.4 hr, median = 13.0 hr). CTA radiation exposure was greater in Alberta compared to other centres (5.7 mSv vs 2.8-5.4 mSv) whereas it was similar for NCCT and CTP. Contrast doses for CTA and CTP were similar between Alberta (CTA = 50-70 mL, CTP = 45 mL) and other centres (CTA = 40-150 mL, CTP = 30-60 mL). Lastly, the number of CT and MRI scanners per million population and cost of operating them was similar worldwide. Conclusions: In conclusion, no studies were identified that provide a comprehensive overview of all six factors relevant to diagnostic imaging in acute stroke. The Alberta metadata fills in this literature gap, specifically in the context of Canadian stroke care.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.047 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".