Surgical and multimodal approaches to right-sided cardiac tumours
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cardiac tumours present significant clinical challenges due to their wide differential, complex anatomical and physiological implications, as well as the potential for widespread invasion in the case of malignancies. This review synthesizes recent findings surrounding the diagnosis and management of specifically right-sided cardiac tumours, with a particular focus on surgical resection and reconstructive techniques. RECENT FINDINGS: Management of cardiac tumours can be categorized into three key phases. First: early and accurate diagnosis is critical for improving outcomes, especially in malignancies. Advances in imaging modalities like MRI, CT, PET-CT, and biopsy techniques enhance diagnostic accuracy. Second: surgical resection is a cornerstone treatment for both benign and malignant right-sided cardiac tumours. Surgery is often curative for benign tumours, while for malignant tumours, R0 resection (complete microscopic removal) in appropriate candidates correlates with better survival. Third: managing cardiac malignancies necessitates a multidisciplinary approach, integrating additional therapies such as chemotherapy, radiation, and emerging immunotherapies tailored to patient and tumour characteristics. SUMMARY: Managing right-sided cardiac tumours demands interdisciplinary expertise. Standardized protocols are limited by the rarity of cases and insufficient high-quality data. International collaboration and sharing of experiences through prospective registries and clinical studies are essential to advancing knowledge and improving patient outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".