Gadofosveset trisodium in the investigation of focal liver lesions in noncirrhotic liver: Early experience
Bibliographic record
Abstract
PURPOSE: To investigate enhancement of hepatic lesions with gadofosveset trisodium (Gdfos) (Ablavar) (blood pool agent) compared with the extracellular gadolinium contrast agent (EcGd) gadobutrol (Gadovist). MATERIALS AND METHODS: The prospective study was approved by the Institutional Research Ethics Board with informed consent. Twelve patients underwent magnetic resonance imaging (MRI) with EcGd followed by MRI with Gdfos. For each agent qualitative analysis described the pattern of enhancement and quantitative analysis was performed using lesion-to-liver contrast-to-noise (CNR) ratio. Paired and unpaired Student's t-test used. RESULTS: Twelve hemangiomas, four metastases, one cyst, two focal nodular hyperplasia (FNH), and three adenomas were found. Cyst, FNH, adenomas, and hemangiomas demonstrated the classic pattern of enhancement with both agents. Hemangiomas demonstrated retention of contrast with both agents and their CNR was not statistically different (P > 0.05). Metastases demonstrated retention of contrast on delayed phase with EcGd. Retention of contrast was not seen in metastases with Gdfos. CNR of metastases with Gdfos was statistically lower than CNR of metastases with EcGd (P = 0.005). CNR of hemangiomata and metastases on delayed phase were significantly different (P = 0.0008) with Gdfos, but similar with EcGd (P = 0.4). CONCLUSION: Hemangiomas accumulate Gdfos on delayed phase and metastases do not, a key additional differentiating feature. Liver imaging with Gdfos may improve characterization of liver lesions.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".