A prediction model to refine the timing of an early second‐look laparoscopic exploration in patients with colon cancer at high risk of early peritoneal metastasis recurrence
Bibliographic record
Abstract
BACKGROUND: In patients at high risk of peritoneal metastasis (PM) recurrence following surgical treatment of colon cancer (CC), second-look laparoscopic exploration (SLLE) is mandatory; however, the best timing is unknown. We created a tool to refine the timing of early SLLE in patients at high risk of PM recurrence. METHODS: This international cohort study included patients who underwent CC surgery between 2009 and 2020. All patients had PM recurrence. Factors associated with PM-free survival (PMFS) were assessed using Cox regression. The primary endpoint was early PM recurrence defined as a PMFS of <6 months. A model (logistic regression) was fitted and corrected using bootstrap. RESULTS: In total, 235 patients were included. The median PMFS was 13 (IQR, 8-22) months, and 15.7% of the patients experienced an early PM recurrence. Synchronous limited PM and/or ovarian metastasis (hazard ratio [HR]: 2.50; 95% confidence interval [CI]: [1.66-3.78]; p < 0.001) were associated with a very high-risk status requiring SLLE. T4 (HR: 1.47; 95% CI: [1.03-2.11]; p = 0.036), transverse tumor localization (HR: 0.35; 95% CI: [0.17-0.69]; p = 0.002), emergency surgery (HR: 2.06; 95% CI: [1.36-3.13]; p < 0.001), mucinous subtype (HR: 0.50; 95% CI [0.30, 0.82]; p = 0.006), microsatellite instability (HR: 2.29; 95% CI [1.06, 4.93]; p = 0.036), KRAS mutation (HR: 1.78; 95% CI: [1.24-2.55]; p = 0.002), and complete protocol of adjuvant chemotherapy (HR: 0.93; 95% CI: [0.89-0.96]; p < 0.001) were also prognostic factors for PMFS. Thus, a model was fitted (area under the curve: 0.87; 95% CI: [0.82-0.92]) for prediction, and a cutoff of 150 points was identified to classify patients at high risk of early PM recurrence. CONCLUSION: Using a nomogram, eight prognostic factors were identified to select patients at high risk for early PM recurrence objectively. Patients reaching 150 points could benefit from an early SLLE.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".