P.067 Focused, bedside cardiac ultrasound in stroke: a feasibility study
Bibliographic record
Abstract
Background: Canadian Stroke Best Practice Recommendations recommend both cardiac monitoring and transthoracic echocardiography (TTE) to assess for cardioembolic sources of stroke. TTE has a diagnostic yield, which is historically low at 5-10%. The goal of this project was to evaluate the practicality of a bedside, focused approach to TTE in ischemic stroke. Methods: A cross-sectional study evaluating patients undergoing echocardiography for evidence of possible cardioembolic stroke was developed. It compared the standard and focused TTE imaging approaches. Of the 61 patients reported, data is currently available for 15 participants. Independent samples t-test were performed to compare measurements. Results: Mean time to finish image acquisition for the focused, bedside TTE was significantly shorter than the complete TTE (12 min or less vs 30 min or more) (p<0.0001). No cardiac sources of stroke were found by either mechanism in this cohort, representing 100% agreement between the two modalities. Conclusions: Focused, bedside echocardiography studies are quicker to execute and employ more affordable, portable, digital TTE devices. The test is done at bedside, reducing the need for patient transport. Image acquisition takes approximately half the time to obtain, potentially allowing for more rapid clinical decision making and facilitation of discharge from hospital.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".