Novel Approach to Proficiency Testing Highlights Key Practice Variations in Cancer Biomarker Delivery
Bibliographic record
Abstract
Biomarkers are fundamental to modern oncology practice, forming a close link to pathology practice. Pathology results must be accurate, timely, comprehensive, and comprehendible. External proficiency testing is a key tool in maintaining biomarker quality. Here, we demonstrate the feasibility and utility of a novel end-to-end proficiency testing exercise exploring accuracy, turnaround time, and communication. Challenge specimens were made using resected colon cancer tissue, each paired with a fictional clinical vignette, and distributed to participants who were asked to provide all molecular testing required and return a final report for each case upon completion. Reports were redistributed to an assessor team including medical oncologists, each of whom was asked to recommend a systemic therapy based on each lab’s biomarker report. Participants were graded based on their ability to guide oncologists to the correct treatment. Eight laboratories participated. Three laboratories were found to have suboptimal results, two leading oncologists to incorrect therapeutic prescriptions, and one withdrawn. Turnaround time ranged from 6 to 86 days (median 24). Substantial qualitative reporting differences were identified. This study demonstrates the feasibility of end-to-end proficiency testing. The approach provides considerable value beyond analytic accuracy, including specimen management, turnaround time, and communication of results. Results suggest that reporting differences may lead to treatment disparities. This style of quality assurance will help reinforce good practices critical to the delivery of precision cancer care.
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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 | 0.143 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".