Ultrasonido con contraste de masas hepáticas
Bibliographic record
Abstract
"El ultrasonido (US) con contraste constituye una poderosa herramienta diagnóstica en la caracterización de masas hepáticas. Las modernas técnicas de ultrasonido con contraste presentan alta sensibilidad con dosis bajas de contraste, lo que juntamente con la técnica de supresión de ecos resulta en imágenes de muy alta resolución temporal y espacial, propiciando el resurgimiento de la ultrasonografía en la era de la tomografía computada (TC) y de la resonancia magnética (RM). El agente de contraste utilizado es muy seguro, sin efectos de nefrotoxicidad y apropiado para pacientes con función renal disminuida. La posibilidad de utilizar múltiples inyecciones de micro burbujas y de realizar observaciones reiteradas de patrones de vascularización de las masas hepáticas, la convierten en una herramienta diagnóstica segura y confiable en la resolución de lesiones hepáticas indeterminadas previo a la TC y RM. En este artículo describimos los principios básicos del US con contraste, consideraciones prácticas en la realización de los estudios, debilidades y fortalezas del método en comparación con la TC y RM y patrones de vascularización en las cinco masas hepáticas más comunes: hemangioma, hiperplasia nodular focal, adenoma, carcinoma hepatocelular y metástasis."
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".