Can preoperative liver MRI with gadoxetic acid help reduce open-close laparotomies for curative intent pancreatic cancer surgery?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To evaluate gadoxetic acid-enhanced liver MRI (EOB-MRI) versus contrast-enhanced computed tomography (CECT) for preoperative detection of liver metastasis (LM) and reduction of open-close laparotomies for pancreatic ductal adenocarcinoma (PDAC). METHODS: Sixty-six patients with PDAC had undergone preoperative EOB-MRI and CECT. LM detection by EOB-MRI and CECT and their impact on surgical planning, open-close laparotomies were compared by clinical and radiology reports and retrospective analysis of imaging by two blinded independent readers. Histopathology or imaging follow-up was the reference standard. Statistical analysis was performed at patient and lesion levels with two-sided McNemar tests. RESULTS: EOB-MRI showed higher sensitivity versus CECT (71.7% [62.1-80.0] vs. 34% [25.0-43.8]; p = 0.009), comparable specificity (98.6%, [96.9-99.5] vs. 100%, [99.1-100], and higher AUROC (85.1%, [80.4-89.9] vs. 66.9%, [60.9-73.1]) for LM detection. An incremental 7.6% of patients were excluded from surgery with a potential reduction of up to 13.6% in futile open-close laparotomies due to LM detected on EOB-MRI only. CONCLUSIONS: Preoperative EOB-MRI has superior diagnostic performance in detecting LM from PDAC. This better informs surgical eligibility with potential reduction of futile open-close laparotomies from attempted curative intent pancreatic cancer surgery.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it