Implementation of lung cancer nurse coordinator significantly enhances patient care and delivery of appropriate chemotherapy.
Bibliographic record
Abstract
e17708 Background: Involvement of nurse coordinators (NC) in oncology care is becoming increasingly common to facilitate more timely access to treatment for patients. The NC role in lung cancer at the BC Cancer Agency (BCCA) involves developing algorithms for staging investigations, molecular tests and referrals. BC has a publicly funded health care system and reflex molecular testing is not available. The purpose was to evaluate referral practice, timelines and molecular testing for advanced NSCLC patients, pre and post implementation of a nurse triage coordinator. Methods: The study included patients referred to the BCCA – Vancouver Centre with advanced NSCLC in 2011 baseline and 2014 after the implementation of a NC. EGFR and ALK testing is available for stage IIIB/IV non squamous NSCLC. Referral patterns, systemic therapy/radiotherapy (XRT), timelines, molecular testing parameters were collected. Results: 408 patients were included: 212 in 2011, 196 in 2014. Medical oncology (MO) endpoints comparing 2011 to 2014: referral rates remained the same, the proportion who received systemic treatment increased 57% vs 69% (p = 0.05). Time from referral to MO consult 18 d vs 15.5 d (p = 0.11), referral to systemic therapy delivery was reduced 48 d vs 38 d (p = 0.016). Molecular testing: time from referral to EGFR result was reduced 34 d vs 20 d (p < 0.001), EGFR result available at MO consult increased 6% vs 37% (p < 0.001), rate of molecular testing increased 62% vs 91% (p < 0.001), EGFR mutation positive (19% vs 26% p = 0.26). For radiation oncology (RO) endpoints: RO consults 87% vs 80% (p = 0.05), the same proportion of patients received XRT (91% vs 87%). Time from referral to RO consult 10 d vs 8 d (p = 0.005), referral to XRT 18 d vs 11.5 d (P < 0.001). Conclusions: Implementation of a NC reduced wait times from referral to treatment for MO and RO. The proportion of patients who underwent molecular testing increased and the EGFR positivity rate remained the same, therefore more patients received appropriate first line targeted therapy. Participation of a nurse coordinator during triage activities suggests that physician, diagnostic and clinical resources are more correctly allocated.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".