Overall approaches to cardiac tumors: Still an unsolved enigma?
Bibliographic record
Abstract
Cardiac tumors are neoplasms involving heart structures at any level, meaning the myocardium, valves, and cardiac chambers. When considering cardiac masses, it is not uncommon for surgeons to be surprised when they diagnose one. The real incidence of this complex group of diseases has been explored only after cardiac diagnostic tools became more appropriate. Despite differential diagnosis being relevant, surgical indication is usually requested for all malignant cardiac tumors and also for many types of benign tumors. The development of cardiac imaging techniques, therefore, has been the key point for a better understanding of the history of cardiac tumors and especially of the relevance of surgical indication in such conditions. Systematic and combined applications of echocardiography, cardiac computed tomography and magnetic resonance allow in the majority of case a clear definition of the nature of a newly discovered cardiac mass. The presence of a Li-Fraumeni syndrome seems to be the trigger aspect in accelerating the propensity of developing a cardiac tumor. Despite the revolutionary usefulness of the cardiac imaging techniques available, it is still considered a hazard to diagnose a malignant cardiac mass just with radiological imaging; the mainstay of the final diagnosis stands in surgical excision of the mass and histopathological report.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".