Integration of a Nurse Navigator into the Triage Process for Patients with Non-Small-Cell Lung Cancer: Creating Systematic Improvements in Patient Care
Bibliographic record
Abstract
Nurse navigation is a developing facet of oncology care. The concept of patient navigation was originally created in 1990 at the Harlem Hospital Center in New York City as a strategy to assist vulnerable and socially disadvantaged populations with timely access to breast cancer care. Since the mid-1990s, navigation programs have expanded to include many patient populations that require specialized management and prompt access to diagnostic and clinical resources. Advanced non-small-cell lung cancer is ideally suited for navigation to facilitate efficient assessment in this fragile patient population and to ensure timely results of molecular tests for first-line therapy with appropriately targeted agents. At the BC Cancer Agency, nurse navigator involvement with thoracic oncology triage has been demonstrated to increase the proportion of patients receiving systemic treatment, to shorten the time to delivery of systemic treatment, and to increase the rate of molecular testing and the number of patients with molecular testing results available at time of initial consultation. Insights gained through the start-up process are briefly discussed, and a framework for implementation at other institutions is outlined.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".