Systematic review and meta-analysis of studies comparing baseline D-dimer level in stroke patients with or without cancer: Strength of current evidence
Bibliographic record
Abstract
Objectives: D-dimer levels are increased in stroke and cancer. Cancer patients are at a higher risk of stroke. However, the evidence is unclear if high D-dimer in stroke patients can suggest the diagnosis of concomitant cancer or the development of stroke in a cancer patient. The objective is to assess the evidence available on the baseline D-dimer level in stroke patients with and without cancer. Materials and Methods: We conducted the systematic review and meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. We searched PUBMED, Cochrane, ScienceDirect, and Scopus for potentially eligible articles published till June 2023. All the review steps were iterative and done independently by two reviewers. The Newcastle-Ottawa scale tool was used to assess the quality of included studies for case control and cohort studies and the Agency for Healthcare Research and Quality tool for cross-sectional studies. The qualitative synthesis is presented narratively, and quantitative synthesis is shown in the forest plot using the random effects model. I 2 of more than 60% was considered as high heterogeneity. Results: The searches from all the databases yielded 495 articles. After the study selection process, six papers were found eligible for inclusion in the qualitative and quantitative synthesis. In the present systematic review, 2651 patients with ischemic infarcts are included of which 404 (13.97%) patients had active cancer while 2247 (86.02%) did not. The studies included were of high quality and low risk of bias. There were significantly higher baseline D-dimer levels in stroke patients with cancer than in non-cancer patients with a mean difference of 4.84 (3.07–6.60) P < 0.00001. Conclusion: D-dimer is a simple and relatively non-expensive biomarker that is increased to significant levels in stroke patients, who have cancer and therefore may be a tool to predict through screening for active or occult cancer in stroke patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".