Implementing Next-Generation Sequencing Process Changes to Increase Capacity and Improve Timeliness of Molecular Biomarker Profiling for Lung Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Faced with expansion of molecular tumor biomarker profiling, the molecular genetics laboratory at Kingston Health Science Centre experienced significant pressures to maintain the provincially mandated 2-week turnaround time (TAT) for lung cancer (LC) patients. We used quality improvement methodology to identify opportunities for improved efficiencies and report the impact of the initiative. METHODS: We set a target of reducing average TAT from accessioning to clinical molecular lab report for LC patients. Process measures included percentage of cases reaching TAT within target and number of cases. We developed a value stream map and used lean methodology to identify baseline inefficiencies. Plan-Do-Study-Act cycles were implemented to streamline, standardize, and automate laboratory workflows. Statistical process control (SPC) charts assessed for significance by special cause variation. RESULTS: A total of 257 LC cases were included (39 baseline January-May 2021; 218 post-expansion of testing June 2021). The average time for baseline TAT was 12.8 days, peaking at 23.4 days after expansion of testing, and improved to 13.9 days following improvement interventions, demonstrating statistical significance by special cause variation (nonrandom variation) on SPC charts. CONCLUSIONS: The implementation of standardized manual and automated laboratory processes improved timeliness of biomarker reporting despite the increasing volume of testing at our center.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".