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.
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 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 | 0.001 | 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) | 0.000 | 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".