A scoping review protocol on diagnostic strategies to detect occult malignancies in individuals with ischemic stroke
Bibliographic record
Abstract
BACKGROUND: Emerging data show an increased risk of ischemic stroke in patients with a new diagnosis of cancer. As the risk of stroke begins to increase 150 days before cancer is diagnosed, stroke may be the first clinical manifestation of undiagnosed cancer. About 6% of patients with cryptogenic ischemic stroke (unknown etiology after diagnostic evaluations) are diagnosed with cancer within one year. However, the optimal cancer screening strategy in this population is not known. We aim to conduct a scoping review of screening strategies for occult cancer in individuals with ischemic stroke. METHODS: Electronic databases including MEDLINE (Ovid), EMBASE (Ovid), CINAHL (EBSCOhost) and Scopus will be systematically searched to identify articles that report on screening strategies for occult cancer in individuals with ischemic stroke. At least two investigators will independently perform two-stage study selection consisting of title/abstract screening and full-text review, followed by data extraction. Thereafter, a thematic analysis will be conducted to provide an overview of what diagnostic tests/strategies have been used, and their clinical utility in terms of positive and negative predictive value (when available). CONCLUSION: We anticipate that the findings of this scoping review will identify strategies used to detect occult cancer in individuals with ischemic stroke and summarize their clinical utility (if reported). Addressing this knowledge gap will help guide the development of future clinical trials on occult cancer screening patients with ischemic stroke.
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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 | 0.109 | 0.114 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.079 | 0.013 |
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".