Referred molecular testing as a barrier to optimal treatment decision making in metastatic non‐small cell lung cancer: Experience at a tertiary academic institution in Canada
Bibliographic record
Abstract
BACKGROUND: Molecular testing is critical to guiding treatment approaches in patients with metastatic non-small cell lung cancer (mNSCLC), with testing delays adversely impacting the timeliness of treatment decisions. Here, we aimed to evaluate the time from initial mNSCLC diagnosis to treatment decision (TTD) following implementation of in-house EGFR, ALK, and PD-L1 testing at our institution. METHODS: We conducted a retrospective chart review of 165 patients (send-out testing, n = 92; in-house testing, n = 73) with newly diagnosed mNSCLC treated at our institution. Data were compared during the send-out (March 2017-May 2019) and in-house (July 2019-March 2021) testing periods. We performed a detailed workflow analysis to provide insight on the pre-analytic, analytic, and post-analytic intervals that constituted the total TTD. RESULTS: TTD was significantly shorter with in-house testing (10 days vs. 18 days, p < 0.0001), driven largely by decreased internal handling and specimen transit times (2 days vs. 3 days, p < 0.0001) and laboratory turnaround times (TAT, 3 days vs. 8 days, p < 0.0001), with 96% of in-house cases meeting the international guideline of a ≤ 10-day intra-laboratory TAT (vs. 74% send-out, p < 0.001). Eighty-eight percent of patients with in-house testing had results available at their first oncology consultation (vs. 52% send-out, p < 0.0001), and all patients with in-house testing had results available at the time of treatment decision (vs. 86% send-out, p = 0.57). CONCLUSION: Our results demonstrate the advantages of in-house biomarker testing for mNSCLC at a tertiary oncology center. Incorporation of in-house testing may reduce barriers to offering personalized medicine by improving the time to optimal systemic therapy decision.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".