Impact of pre-operative abdominal MRI on survival for patients with resected pancreatic carcinoma: a population-based study
Bibliographic record
Abstract
Background: This study determined the impact of pre-operative abdominal MRI on all-cause mortality for patients with resected PDAC. Methods: All adult (≥18 years) PDAC patients who underwent pancreatectomy between January 2011 and December 2022 in Ontario, Canada, were identified for this population-based cohort study (ICD-O-3 codes: C250, C251, C252, C253, C257, C258). Patient demographics, comorbidities, PDAC stage, medical and surgical management, and survival data were sourced from multiple linked provincial administrative databases at ICES. All-cause mortality was compared between patients with and without a pre-operative abdominal MRI after controlling for multiple covariates. Findings: A cohort of 4579 patients consisted of 2432 men (53.1%) and 2147 women (46.9%) with a mean age of 65.2 years (standard deviation: 11.2 years); 2998 (65.5%) died while 1581 (34.5%) survived. Median follow-up duration post-resection was 22.4 months (interquartile range: 10.8-48.8 months), and median survival post-pancreatectomy was 25.9 months (95% confidence interval [95% CI]: 24.8, 27.5). Patients who underwent a pre-operative abdominal MRI had a median survival of 33.1 months (95% CI: 30.7, 37.2) compared to 21.1 months (95% CI: 19.8, 22.6) for all others. A total of 2354/4579 (51.4%) patients underwent a pre-operative abdominal MRI, which was associated with a 17.2% (95% CI: 11.0, 23.1) decrease in the rate of all-cause mortality, with an adjusted hazard ratio (aHR) of 0.828 (95% CI: 0.769, 0.890). Interpretation: Pre-operative abdominal MRI was associated with improved overall survival for PDAC patients who underwent pancreatectomy, possibly due to better detection of liver metastases than CT. Funding: Northern Ontario Academic Medicine Association (NOAMA) Clinical Innovation Fund.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".