Abstract W MP106: Temporal Trends in Stroke Investigations and Their Effect on Management
Bibliographic record
Abstract
Background: Current guidelines recommend that patients with suspected stroke undergo neuroimaging to confirm the diagnosis and that those with ischemic stroke or TIA receive carotid imaging and cardiac investigations to determine stroke etiology. It is not known how rates of investigations have changed over time, and if those trends have led to changes in medical or surgical management. We used a clinical stroke registry to evaluate temporal trends in stroke investigations in Ontario, Canada. Methods: We used the Ontario Stroke Registry to identify patients who presented with stroke and TIA to 11 stroke centers between 2003 and 2012. The primary outcome was the proportion of patients who received CT, MRI, CTA, MRA, carotid doppler, and echocardiography in each year. Secondary outcomes were (1) the total number of neuroimaging procedures per patient; (2) the proportion of patients with ischemic stroke/TIA prescribed antithrombotics at discharge; and (3) the proportion who had carotid revascularization. The characteristics of study participants, rates of investigations and interventions, and test for trends over one year intervals were completed using a Cochran-Armitage trend test. Results: The study sample included 42,738 patients. From 2003 to 2012, the proportion receiving any type of neuroimaging increased from 96% to 99%, those receiving an MRI increased from 10% to 49%, those receiving carotid imaging increased from 62% to 88% and those receiving echocardiography increased from 52% to 70% (P<0.0001 for all comparisons). The total number of neuroimaging procedures per patient also increased, from a median of 1 in 2003 to 3 in 2011 (P<0.0001), and with almost 40% undergoing three or more neuroimaging procedures in 2011. In those with ischemic stroke or TIA, rates of antithrombotic therapy increased from 83% to 91%, as did the rate of carotid endarterectomy, from 0.7% to 0.8%. (P< 0.009). Conclusions: Rates of neuroimaging, carotid imaging and cardiac investigations after stroke have markedly increased over time. It is uncertain whether the increased rates of MRI and neuroimaging per patient are associated with improved outcomes. Further research is needed to evaluate the cost-effectiveness of current patterns of investigations following 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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".