Case Report: Surgical resection of giant ventricular fibroma in an infant utilizing 3D imaging guidance
Bibliographic record
Abstract
Primary cardiac tumors are extremely rare, with fibromas being one of the more prevalent type primary cardiac tumors in infants and children. Cardiac fibromas present a high risk of fatal arrhythmia and sudden death, hence more aggressive surgical treatment approaches are typically employed. However, certain populations, such as asymptomatic infants and young children in the early stages, require extra caution. We present the case of a patient with a giant fibroma of the heart detected during fetal development, who was followed up until the age of 5 months before undergoing surgical resection. Prior to surgery, we employed three-dimensional (3D) imaging technology to acquire a deeper understanding of the anatomical nature of cardiac tumors. We then devised a comprehensive surgical strategy to minimize the risk of damage to large blood vessels during surgery and maximize preservation of myocardial tissue. Following surgical resection of the tumor, cardiac dysfunction was managed with extracorporeal membrane oxygenation (ECMO) continuous adjuvant therapy, and conventional vasodilators such as dopamine and nitroglycerin were ad. The patient recovered well without any serious complications. This case highlights the significance of timely surgical intervention, combined with 3D imaging to develop a meticulous surgical plan and early use of ECMO to maintain cardiac function in patients with postoperative cardiac dysfunction. This can help to ensure the safety and effectiveness of giant cardiac fibromas resection in infants and young children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".