Incidence of stroke in the first year after diagnosis of cancer—A protocol for systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: There is an increased risk of stroke in patients with cancer-this risk is particularly heightened around the time of cancer diagnosis, although no studies have systematically quantified this risk in the literature. Patients newly diagnosed with cancer without prior stroke represent a highly susceptible population in whom there is a window of opportunity to study and implement primary prevention strategies. Therefore, the objective of this systematic review and meta-analysis is to identify the cumulative incidence of ischemic and hemorrhagic strokes during the first year after a diagnosis of cancer. METHODS AND ANALYSIS: MEDLINE, EMBASE, and PubMed will be searched with the assistance from a medical information specialist, from 1980 until present. Eligible studies will include observational studies that have enrolled adult patients newly diagnosed with cancer and report outcomes of stroke during the first year of cancer diagnosis. We will exclude all randomized and non-randomized interventional studies. Data on participant characteristics, study design, baseline characteristics, and outcome characteristics will be extracted. Study quality will be assessed using the Newcastle-Ottawa Scale for cohort studies, and heterogeneity will be assessed using the I2 statistic. Pooled cumulative incidence will be calculated for ischemic and hemorrhagic strokes separately using a random-effects model. ETHICS AND DISSEMINATION: No formal research ethics approval is necessary as primary data collection will not be done. We will disseminate our findings through scientific conference presentations, peer-reviewed publications, and social media/the press. The findings from this review will inform clinicians and patients regarding the risk of stroke in patients newly diagnosed with cancer by quantifying the cumulative incidence of each subtype of stroke during the first year after a diagnosis of cancer. This represents a window of opportunity to implement prevention strategies in a susceptible population. REGISTRATION ID WITH OPEN SCIENCE FRAMEWORK: osf.io/ucwy9.
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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 | 0.066 | 0.109 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".