Bibliographic record
Abstract
PURPOSE OF REVIEW: This review provides an update of the classification in the classification of vascular anomalies since April 2014 at the International Society for the Study of Vascular Anomalies meeting in Melbourne, Australia. RECENT FINDINGS: The reader will become familiar with how to diagnose the major vascular malformations, including capillary, venous, arteriovenous, and lymphatic and combinations thereof. In addition, vascular malformation syndromes, including those with overgrowth, will be clarified. SUMMARY: Vascular malformations are common. Capillary malformations are now better understood through an updated classification. Verrucous hemangioma is truly a venulocapillary malformation that extends into the subcutis. PIK3Ca-Related Overgrowth Syndromes encompass Klippel-Trenaunay, Congenital Lipomatous Asymmetric Overgrowth of the Trunk with Lymphatic, Capillary, Venous, and Combined-Type Vascular Malformations, Epidermal Nevi, Scoliosis/Skeletal and Spinal Anomalies, Megalencephaly-Capillary Malformation-Polymicrogyria Syndrome (M-CAP), fibroadipose hyperplasia, and macrodactyly. Yet another syndrome should be highlighted: Capillary Malformation of the Lower Lip, Lymphatic Malformation of the Face and Neck, Asymmetry and Partial/Generalized Overgrowth. Knowledge of the genetic basis of vascular malformations will lead to future treatments.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".