Abstract TMP37: Physicians’ Preferences in the Management of Silent Stroke: Results From a Worldwide Survey
Bibliographic record
Abstract
Introduction: Patients with silent cerebral infarction (SCI) or microbleeds (MB) are at higher risk of developing a recurrent vascular event or suffering a hemorrhage as a consequence of thrombolysis or antithrombotic treatment. Additionally, the presence of occult cerebrovascular lesions on imaging may alter therapeutic or diagnostic recommendations. However, clinical guidelines are lacking. Hypothesis: We hypothesize that a wide variability in physician diagnostic and treatment preferences exist for individuals with SCI and MB. Methods: Respondents were practicing neurologists with expertise in stroke care identified from WSO, CSC, Vas-Cog and SORCan. Those who agreed to participate were sent a questionnaire which was completed online. Participants randomly received 10 cases from a pool of 20, assessing perception of risk for later stroke and treatment preferences, as well as demographic questions. Results: Among the 252 participants, 35 (13.9%) were excluded for incomplete responses. Of 217 participants included in this analysis, two thirds of participants would proceed with revascularization therapies (tPA: 40%, endovascular: 8%, combined: 22%) for a case scenario with acute global aphasia (NIHSS 6-7) and remote bilateral MB. Additionally, physicians would order an echocardiogram regardless of the location of the SCI (cortical SCI: 56%, lacunar SCI: 44%; p>0.05). In a case-scenario with mild hypertension and 4 deep microbleeds presented with difficulty with balance, almost half of respondents did not recommend antiplatelet drugs or statins (40.2%), and 20% recommended both antiplatelet and statins. NOACS were the most commonly recommended antithrombotic treatment in a case-scenario with atrial fibrillation (AF), SCI, and a remote lobar intracerebral hemorrhage (46.4%), followed by aspirin (20.9%), no treatment (17.3%). Conclusion: There is a wide variability in the management of SCI and MB among stroke experts. The present study highlights the need for further research in treatment efficacy, as well as recommendations from scientific organizations to guide clinicians in the management of high risk patients with SCI and MB.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".