Effect of PET‐CT on disease recurrence and management in patients with potentially resectable colorectal cancer liver metastases. Long‐term results of a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Preoperative evaluation of resectable colorectal cancer liver metastases with positron emission tomography (PET) combined with computed tomography (PET-CT) is used extensively. The PETCAM trial evaluated the effect of PET-CT (intervention) vs no PET-CT (control) on surgical management. PET-CT resulted in 8% change in surgical management, therefore, we aimed to compare long-term outcomes (disease-free [DFS], overall survival [OS]). METHODS: Trial recruitment (2005-2010) had prospective follow-up until 2013. Events from 2013 to 2017 were collected retrospectively. Survival was described by the Kaplan-Meier method and compared with log-rank test. Oncologic risk factors were calculated using Cox proportional hazard models. RESULTS: Among 404 patients randomized, there were no differences in DFS (hazard ratio [HR] = 1.13; 95% confidence interval [CI], 0.89 to 1.43) or OS (HR, 1.02; 95% CI, 0.78-1.32) between groups. For all patients randomized, median DFS (PET-CT vs no PET-CT) was 16 months (95% CI, 13-18) and 15 months (95% CI, 11-22), P = .33. For patients who underwent liver resection (n = 368), DFS (17 vs 16 months, P = .51) and OS (58 months vs 52 months, P = .90) were similar between groups, respectively. Risk factors for DFS and OS were age, tumor size, node-positive disease, extrahepatic metastases and disease-free duration. CONCLUSION: Preoperative PET-CT changes surgical management in a small percentage of cases, without effect on recurrence rates or long-term survival.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".