Exploring the Extent of Variance in the Development, Prognosis, and Outcome Between Primary and Secondary Cardiac Tumours: A Systematic Review
Bibliographic record
Abstract
This review highlights the role of genetics and cellular changes within cardiac muscle in explaining the low prevalence of cardiac tumours, and the preferential development of specific neoplastic subtypes as compared to others. The varying features of primary and secondary cardiac neoplasms are highlighted, with an extended focus on the paediatric population. By analysing past literature, medical interventions, prognostic outcomes, and pathophysiological mechanisms behind cardiac neoplasms are identified. The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and employed a thorough Medical Subject Headings (MeSH) search; 18 studies were included in the final analysis. We applied our inclusion criteria to retrieve studies in the English language published from 2000 to 2025. This review primarily includes human studies, with some evidence from animal studies, which were peer-reviewed and are available as full texts. Overall data on 628 patients with cardiac neoplasms were included to discuss the divergent properties of primary cardiac tumours (PCTs) and metastatic cardiac tumours (MCTs). The paper discusses the properties of cellular division within cardiac cells and analyses the properties of muscle cells to explain the mechanism behind the low prevalence of cardiac cancers.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".