Comparing Enrolees with Non-Enrolees of Cancer-Patient Navigation at End of Life
Bibliographic record
Abstract
Background: Cancer-patient navigators who are oncology nurses support and connect patients to resources throughout the cancer care trajectory, including end of life. Although qualitative and cohort studies of navigated patients have been reported, no population-based studies were found. The present population-based study compared demographic, disease, and outcome characteristics for decedents who had been diagnosed with cancer by whether they did or did not see a navigator. Methods: This retrospective study used patient-based administrative data in Nova Scotia (cancer registry, death certificates, navigation visits) to generate descriptive statistics. The study population included all adults diagnosed with cancer who died during 2011-2014 of a cancer or non-cancer cause of death. Results: Of the 7694 study decedents, 74.9% had died of cancer. Of those individuals, 40% had seen a navigator at some point in their disease trajectory. The comparable percentage for those who did not die of cancer was 11.9%. Decedents at the oldest ages had the lowest navigation rates. Navigation rates, time from diagnosis to death, and time from last navigation visit to death varied by disease site. Conclusions: This population-based study of cancer-patient navigation enrolees compared with non-enrolees is the first of its kind. Most findings were consistent with expectations. However, we do not know whether the rates of navigation are consistent with the navigation needs of the population diagnosed with cancer. Because more people are living longer with cancer and because the population is aging, ongoing surveillance of who requires and who is using navigation services is warranted.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".