Optimizing the sequence of biological agents in advanced colorectal cancer: A decision analysis.
Bibliographic record
Abstract
e14171 Background: Patients (pts) with wild-type (WT) K-ras metastatic colorectal cancer(mCRC) may benefit from the following five drugs/classes: fluoropyrimidines (FP), oxaliplatin (O), irinotecan (I), bevacizumab (B) and EGFR inhibitors (EGFRI). However, pending the results of ongoing randomized trials, the optimal sequencing of these agents is unclear. Methods: A Markov model was constructed for a hypothetical cohort of pts with WT K-ras unresectable mCRC to examine the outcomes of 3 different treatment strategies over a time horizon of 5 years. Strategy A: 1st line B+FP+O/I, 2nd line FP+I/O, 3rd line EGFRI monotherapy. Strategy B: 1st line B+FP+O/I, 2nd line FP+I/O, 3rd line EGFRI + I. Strategy C: 1st line EGFRI+FP+O/I, 2nd line B+ FP+I/O, 3rd line best supportive care. Efficacy and probability data were obtained from published clinical trials identified through a systematic review using MEDLINE, EMBASE and the Cochrane Central registry of clinical trials. The primary endpoint was mean overall survival (OS). Results: The results are shown in the table below. One-way sensitivity analyses revealed that strategy C may be preferred to B when less than 30% of pts were eligible to receive 3rd line treatment (base case was 34%), or when the relative benefit of 3rd line EGFRI+I to EGFRI alone had a hazard ratio (HR) of more than 0.57 (base case HR = 0.54). First order micro-simulation (n=100,000) suggested that 77% and 82% of the simulations revealed the differences between strategies B and C to be less than 1 month and 3 months respectively. Only 6% of the simulations suggested strategy B was superior to others by more than 3 months, while only 12% suggested strategy C was superior to others by more than 3 months. Conclusions: All 3 strategies appeared to result in similar OS for pts with unresectable mCRC. Objective evidence regarding the proportion of patients eligible for 3rd line EGFRI treatment in clinical practice would be useful given the implications for a preferred strategy to maximize OS. [Table: see text]
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".