Family Physician Continuity of Care and Emergency Department Use in End-of-Life Cancer Care
Bibliographic record
Abstract
BACKGROUND: Despite cancer patients preferring to spend their last days out-of-hospital, many make difficult visits to the emergency department (ED). Family physician continuity of care has been shown in some clinical situations to reduce ED utilization. OBJECTIVE: To determine if greater family physician continuity of care for cancer patients during the end-of-life is associated with less ED utilization. METHOD: This retrospective, population-based study involved secondary analysis of linked administrative data files for 1992 to 1997. Sources included the Nova Scotia Cancer Registry, Vital Statistics, the Queen Elizabeth II Health Sciences Center Oncology Patient Information System and Palliative Care Program (PCP), Hospital Admissions/Separation data, and Physician Services information. Subjects included adults with a recorded date of cancer diagnosis who died of cancer and who had made at least three visits to a family physician during their last 6 months of life. The relationship between total ED visits and family physician continuity of care, developed using the Modified Modified Continuity Index (MMCI), was examined using negative binomial regression with adjustments for survival, year of death, sex, age, cancer type, region, PCP admission, specialty visits, hospital days, death location, income quintile, and total ambulatory visits. RESULTS: In total, 8702 subjects made 11,551 ED visits (median = 1.0); median MMCI was 0.83. Adjusted results indicate those experiencing low continuity (MMCI < 0.5) made 3.9 times more ED visits (rate ratio [RR] = 3.93; 95% CI [CI] = 3.57-4.34) than those experiencing high continuity (MMCI > or = 0.8) and patients experiencing moderate continuity (MMCI = 0.5-0.8) made twice as many ED visits (RR = 2.28; CI = 2.15-2.42). CONCLUSION: Given this significant association between family physician continuity of care and ED visits during the end-of-life, and given international trends to reform primary care, active planning of strategies to facilitate such continuity should be encouraged.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".