Impact on turnaround times (TAT) among non-squamous non-small cell lung cancer (NSCLC) patients across three time periods with varying biomarker testing techniques.
Bibliographic record
Abstract
9085 Background: Biomarker testing and identification of actionable genomic alterations (AGAs) in NSCLC patients have led to targeted therapy use and improved outcomes, particularly in advanced stages. We sought to evaluate the impact of different testing paradigms on TAT, particularly with respect to treatment decision and time to initiation of treatment, across three time periods. Methods: A retrospective review of stage III or IV non-squamous NSCLC patients at the Princess Margaret Cancer Centre (Toronto, Canada) was conducted. Both de novo advanced and patients with metastatic progressions are included. Cohort 1 (C1; 01/2015 – 01/2017) underwent reflex EGFR (EntroGen) and ALK (5A4 immunohistochemistry, IHC) single gene testing. Cohort 2 (C2; 02/2017 – 09/2020) underwent reflex next generation sequencing (NGS; Trusight Tumor 15), ALK and ROS1 testing. Cohort 3 (C3; 10/2020 – 01/2022) underwent reflex comprehensive NGS (161 genes, Oncomine OCA v3). Descriptive statistics are presented, including AGAs found and TAT from biopsy to result sign-out, and treatment related TAT. Results: Three cohorts of stage III and IV patients were identified, with C1 having proportionally more females (60%), never smokers (42%) with adenocarcinomas (98%). More patients with AGA were identified using NGS with larger panels (42%, 41%, 57%, respectively across C1 – C3). However, there is a longer median time from first oncology visit to treatment (14, 22, 27 days, respectively across C1-C3), and longer biopsy to treatment TAT trended towards more comprehensive testing (34, 38, 40 days, respectively across C1 - C3) in stage IV patients. Conclusions: As more comprehensive biomarker testing became available, more AGAs are identified with an increase in TAT from biopsy to sign-out and treatment start. Despite our institutional policy of reflex testing, future endeavours will be focus on ways to reduce TAT. [Table: see text]
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".