Approach to Biomarker Testing: Perspectives from Various Specialties
Bibliographic record
Abstract
BACKGROUND: Despite its importance for patient outcomes, biomarker testing for lung cancer is not uniformly integrated into the Canadian health care system. To better understand current practice patterns for lung cancer biomarker testing, we assessed physician perspectives by specialty and region. METHODS: A national survey of Canadian lung cancer specialists was conducted to understand their perspectives on biomarker testing in lung cancer. The 11-item survey assessed the current practice and challenges of testing. The survey was sent to 375 specialists. RESULTS: The overall response rate for the survey was 36%. Nearly all specialists reported that knowing tumour genotyping results affects patient outcome and influences the treatment decision. Medical oncologists most commonly initiated molecular testing; however, most respondents suggested a shared model involving medical oncologists and pathologists. More than half of all responding specialists had the perception that fewer than 25% of test results are available for first-line treatment decisions. Identified barriers to routine testing for all lung cancer patients included cost, lack of funding, tissue availability, and sample quality. CONCLUSIONS: There was clear agreement that biomarker testing is important in determining appropriate treatment for patients. There is a need for general consensus on who should initiate molecular testing. Clear clinical guidance for pathologists has to be established for molecular testing, including defining the population to be tested, the timing of testing, and the tests to be performed. Testing could be facilitated by including more information on diagnostic sample requisitions, such as clinical suspicion of primary lung cancer, cancer history, and other samples already collected.
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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 | 0.032 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".