Validation of KRAS Testing for Anti-EGFR Therapeutic Decisions for Patients With Metastatic Colorectal Carcinoma
Bibliographic record
Abstract
CONTEXT: KRAS mutation status is a molecular marker for predicting patient response to treatment with anti-EGFR antibodies (cetuximab and panitumumab) in metastatic colorectal carcinoma. Different approaches may be taken to detect KRAS mutations. There currently are no US Food and Drug Administration-approved assays for the detection of KRAS mutations. For assays that are not approved by the US Food and Drug Administration, the performance characteristics of the assay must be determined and validated by the clinical laboratory before implementation. OBJECTIVE: To provide an example of how a KRAS mutation-analysis assay may be validated in a clinical laboratory. DESIGN: Describing the approach used by an individual laboratory to compare different assays for validation of KRAS mutation analysis in metastatic colon carcinoma. RESULTS: Specific validation data are provided, illustrating how a laboratory established assay performance characteristics for KRAS mutation analysis. CONCLUSIONS: All clinical laboratories must establish several performance specifications mandated by the Clinical Laboratory Improvement Amendments of 1988 before implementation of any laboratory-developed test. Approaches to the validation of such assays may vary among laboratories. We describe an approach used for validation of a KRAS mutation-analysis assay by one laboratory.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".