Implementation of a Lung Cancer Nurse Navigator Enhances Patient Care and Delivery of Systemic Therapy at the British Columbia Cancer Agency, Vancouver
Bibliographic record
Abstract
PURPOSE: A nurse navigator (NN) pilot project for patients with lung cancer was implemented in British Columbia, a publicly funded health-care system. The purpose was to improve referral practices, timelines, and availability of molecular testing for patients with advanced non-small-cell lung cancer (NSCLC). METHODS: Patients with stage IIIB/IV NSCLC referred to the BC Cancer Agency, Vancouver, in 2011 and 2014, pre- and post-implementation of an NN, were included. Referral patterns, systemic therapy, radiotherapy (XRT) timelines, and molecular testing practices were compared. RESULTS: The study included 408 patients: 212 in 2011 and 196 in 2014. Medical oncology (MO) end points comparing 2011 data with 2014 findings revealed that referral rates remained stable, and the proportion of patients who received systemic therapy increased from 57% to 69% (P = .05). Time from referral to MO consult was 18 days in 2011 versus 15.5 days in 2014 (P = .11); referral to systemic treatment was reduced from 48 to 38 days (P = .016). Comparison of molecular testing showed time between referral and the epidermal growth factor (EGFR) result was reduced from 34 days in 2011 to 20 days in 2014 (P < .001); rates of testing increased from 62% to 91%, respectively (P < .001); and EGFR mutation-positive rates were 19% versus 26%, respectively (P = .26). The radiation oncology (RO) end point results were as follows: 87% of patients were referred for RO consults in 2011 versus 80% in 2014 (P = .05), and the same proportion of patients received XRT (91% v 87%, respectively). Time from referral to RO consult decreased from 10 days in 2011 to 8 days in 2014 (P = .005); and referral to XRT in 2011 and 2014 was 18 days versus 11.5 days, respectively (P < .001). CONCLUSION: Implementation of an NN was associated with reduced wait times and increased molecular testing, improving appropriate delivery of first-line targeted therapy. NN involvement facilitates correct allocation of physician and clinical resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".