Frequency and predictors of occult cancer in ischemic stroke: A systematic review and meta-analysis
Bibliographic record
Abstract
BackgroundThe optimal approach for cancer screening after an ischemic stroke remains unclear.AimsWe sought to summarize the existing evidence regarding the frequency and predictors of cancer after an ischemic stroke.Summary of reviewWe searched seven databases from January 1980 to September 2019 for articles reporting malignant tumors and myeloproliferative neoplasms diagnosed after an ischemic stroke (PROSPERO protocol: CRD42019132455). We screened 15,400 records and included 51 articles. The pooled cumulative incidence of cancer within one year after an ischemic stroke was 13.6 per thousand (95% confidence interval [CI], 5.6–24.8), higher in studies focusing on cryptogenic stroke (62.0 per thousand; 95% CI, 13.6–139.3 vs 9.6 per thousand; 95% CI, 4.0–17.3; p = 0.02) and those reporting cancer screening (39.2 per thousand; 95% CI, 16.4–70.6 vs 7.2 per thousand; 95% CI, 2.5–14.1; p = 0.003). Incidence of cancer after stroke was generally higher compared to people without stroke. Most cases were diagnosed within the first few months after stroke. Several predictors of cancer were identified, namely older age, smoking, and involvement of multiple vascular territories as well as elevated C-reactive protein and d-dimers.ConclusionsThe frequency of incident cancer after an ischemic stroke is low, but higher in cryptogenic stroke and after cancer screening. Several predictors may increase the yield of cancer screening after an ischemic stroke. The pooled incidence of post-stroke cancer is likely underestimated, and larger studies with systematic assessment of cancer after stroke are needed to produce more precise and valid estimates.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".