Bridging the gap: measuring the impact of cancer patient navigation on time to CancerCare referral and first treatment for lung cancer
Bibliographic record
Abstract
Background: Lung cancer is the leading cause of cancer deaths worldwide, due to advanced stages of disease at presentation. Healthcare systems are increasingly siloed, resulting in a myriad of care transitions that increase the complexity of care, often resulting in poor patient outcomes. Cancer patient navigation (CPN) emerged in Manitoba, as part of the province’s IN SIXTY initiative, with the general aim of guiding patients through the complexities of the healthcare system to reduce barriers to care, streamline services, and promote efficiencies. Since its inception over a decade ago, there has been no formalized evaluation of IN SIXTY and CPN in Manitoba. Purpose: The aim of this thesis was to measure the impact of CPN in Manitoba on supporting patients with primary lung cancer in achieving timely referral to CancerCare Manitoba (CCMB) and first treatment. Methods: A retrospective, cohort study of Manitoba Cancer Registry and Aria data was completed to compare patients who received care from a nurse navigator as part of CPN to patients who received standard care, on the number of days from lung cancer diagnosis to CCMB referral, and date of first treatment, using multilinear Cox proportional hazards regression analysis. Findings: Patients who received CPN, actualized by nurse navigators, had a statistically significant reduced number of days from lung cancer diagnosis to referral receipt at CCMB, and to the date of first treatment. The timing of CPN, which was found to occur at different points along the cancer continuum, was also found to affect the degree to which CPN impacted timely lung cancer care. CPN implemented prior to lung cancer diagnosis was found to be the most effective model of care. Conclusion: CPN is an effective, nurse-led, model of care, that shortens the time to CCMB referral and first treatment for lung cancer. Further research is needed regarding its impact in other disease sites, and during the suspicion-interval of newly suspected cancer.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".