Malignancy Workup in Cryptogenic Stroke
Bibliographic record
Abstract
Background and Objectives: The diagnostic workup for patients with cryptogenic stroke includes investigating for occult cancer, which is known to increase the risk of stroke. Current guidelines do not provide specific recommendations regarding the optimal approach for occult cancer screening after cryptogenic stroke. We surveyed Canadian stroke and thrombosis physicians to determine current workup preferences for detecting occult cancer after cryptogenic stroke. Methods: We designed and distributed an anonymous online survey targeting physicians who manage patients with cryptogenic stroke through professional memberships of the Canadian Stroke Consortium and Thrombosis Canada. Using 4 clinical scenarios representative of patients with cryptogenic stroke with different ages (younger or older than 50 years) and from both sexes, we asked respondents which tests they routinely recommend when investigating for occult cancer among a list of laboratory investigations, imaging, and procedures. Results were analyzed using descriptive statistics. Results: We received 138 responses to 5 survey questions. The most commonly recommended investigations were complete blood count (79%), creatinine (63%), and coagulation tests (56%), and the most frequently recommended imaging test was CT of the abdomen and pelvis (39%). A minority of respondents indicated they would order guideline-directed age-appropriate cancer screening. Approximately half of surveyed specialists deferred the workup of cancer to a primary care physician, and 12% did not suggest any cancer workup at all. Discussion: This survey of stroke and thrombosis experts found heterogeneity in testing for cancer screening in patients with cryptogenic stroke, with the majority either not screening at all or deferring tests to primary care providers. Our survey highlights the need for better evidence and evidence-based recommendations to guide the approach to cancer screening in this population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".