Biomarker Turnaround Times and Impact on Treatment Decisions in Patients with Advanced Non-Small Cell Lung Carcinoma at a Large Canadian Community Hospital with an Affiliated Regional Cancer Centre
Bibliographic record
Abstract
Background: Timely reporting of molecular biomarkers is critical in guiding optimal treatment decisions in patients with advanced non-small cell lung carcinoma (NSCLC). Any delays along the tissue or treatment pathway may be associated with suboptimal treatment/outcomes and a reduced quality of life. For many centres, biomarkers are tested off-site. Methods: A retrospective chart review of 123 patients with advanced NSCLC seen between 1 June 2021 and 30 June 2022 was conducted. With a focus on core biomarkers (PD L1, EGFR, and ALK), the outcome variables were as follows: total turnaround time (total TAT), divided into pre-laboratory, laboratory, and post-laboratory time intervals, as well as time to treatment decision (TOTD) and time to optimal systemic therapy decision (TOTSD). Results: At first consult, only 20.3% of patients had all core biomarker results available. The median total TAT was significantly longer for non-squamous (non-SCC) than squamous cell carcinoma (SCC) specimens (36.5 versus 22 days, p < 0.001). The median pre-laboratory time for the entire cohort was 5 calendar days. The median laboratory testing time was greater for non-SCC compared to the SCC specimens (23 versus 12 days, p < 0.001). The median time from consult to TOTD was 19 calendar days for the entire cohort. Conclusions: This study emphasizes the need for the expansion of regional resources to meet the clinical needs of advanced NSCLC patients treated at a regional cancer centre which uses an off-site molecular 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".