<i>KRAS</i> and Colorectal Cancer: Ethical and Pragmatic Issues in Effecting Real-Time Change in Oncology Clinical Trials and Practice
Bibliographic record
Abstract
Systemic therapy has led to a median survival time for patients with advanced colorectal cancer (CRC) almost fourfold longer than that expected with best supportive care, an outcome achieved through combining chemotherapeutic and targeted biologic agents. Although the latter can include anti-epidermal growth factor receptor antibodies, such as cetuximab and panitumumab, we now have strong evidence that patients whose tumors harbor mutated KRAS will not benefit from this class of agent. Acceptance of the reliability and importance of the KRAS data took several years to evolve, however, for a variety of reasons. The timeline from the presentation and publication of small, retrospective phase II studies to widespread acceptance of the KRAS predictive value and changes in behavior-specifically, modifications of ongoing national trials in advanced/metastatic CRC, changes in national guidelines and practice patterns, and adjustments to the labeled indications for the monoclonal antibodies-was lengthy. In this commentary, we discuss whether or not the process of data disclosure regarding KRAS status and treatment of advanced CRC patients was effective in permitting timely decisions regarding ongoing publicly funded clinical trials and whether or not such decisions were rational and ethical. The overall goals are to highlight lessons learned regarding early disclosure of clinical trial results, as well as vetting and adoption of new scientific data, and to propose modifications for handling similar situations in the future.
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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 | 0.590 | 0.691 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.051 | 0.048 |
| Insufficient payload (model declined to judge) | 0.002 | 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".