Imaging features and enhancement technique to diagnose and classify intrathoracic Lymphatic-venous malformations: A case report and literature review
Bibliographic record
Abstract
The diagnosis and treatment of pediatric intrathoracic lymphatic-venous malformations (LVM) can be complex due to their rarity, variable presentation and confusing nomenclature in the literature. The International Society for the Study of Vascular Anomalies (ISSVA) has recently (2018) updated their classification to help guide the correct diagnosis, nomenclature and management of such cases. We present the case of a 12-month-old Caucasian female with a lymph-venous malformation (LVM) classified in the updated ISSVA classification as a combined vascular malformation (CLVM) defined as two or more vascular malformations found in one lesion, associated with an underlying "malformation of an individual named vessel". The patient presented with tachypnea, tachycardia and fever. While all the previous cases underwent surgical treatment, our patient was successfully treated with rapamycin and sclerotherapy. Appropriate imaging can aid in the diagnosis of vascular anomalies and in the proper ISSVA classification, saving the patient the need for a biopsy and allow for proper referral to Multidisciplinary Vascular Anomalies centers. The accurate classification can identify cases that can be treated through Interventional Radiology with sclerosing agents and medical therapy as opposed to surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".