Bibliographic record
Abstract
There is a broad spectrum of benign soft tissue tumors in children, including, among others, vascular, fibroblastic/myofibroblastic, and adipocytic lesions as well as pseudotumors. The diagnosis of superficial soft tissue tumors is in many instances made clinically, whereas those with more equivocal presentation and those found in the deeper soft tissues often require imaging. Ultrasonography is the modality of choice for smaller and superficial lesions and is particularly useful for vascular tumors. Magnetic resonance imaging is the modality of choice for the larger and deeper lesions and for those in which ultrasonography is not adequate. Plain radiographs and computed tomography have a very limited role in the evaluation of soft tissue masses. Correlation with clinical history and findings on physical examination are imperative for an appropriate interpretation of the imaging findings. Although certain entities can be confidently diagnosed based on the combination of clinical and imaging findings, histology is frequently required for a definitive diagnosis. The differentiation of benign from malignant tumors is also challenging and often not possible based on the imaging findings. This article reviews the clinical features and imaging findings of the most common benign pediatric soft tissue tumors.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".