Continuity of physician care over the last year of life for different cause-of-death categories: a retrospective population-based study
Bibliographic record
Abstract
BACKGROUND: The mix of care provided by family physicians, specialists and palliative care physicians can vary by the illnesses leading to death, which may result in disruptions of continuity of care at the end of life. We measured continuity of outpatient physician care in the last year of life across differing causes of death and assessed factors associated with higher continuity. METHODS: We conducted a retrospective descriptive study of adults who died in Ontario between 2013 and 2018, using linked provincial health administrative data. We calculated 3 measures of continuity (usual provider, Bice-Boxerman and sequential continuity), which range from 0 to 1, from outpatient physician visits over the last year of life for terminal illness, organ failure, frailty, sudden death and other causes of death. We used multivariable logistic regression models to evaluate associations between characteristics and a continuity score of 0.5 or greater. RESULTS: Among the 417 628 decedents, we found that mean usual provider, Bice-Boxerman and sequential continuity indices were 0.37, 0.30 and 0.37, respectively, with continuity being the lowest for those with terminal illness (0.27, 0.23 and 0.33, respectively). Higher number of comorbidities, higher neighbourhood income quintile and all non-sudden death categories were associated with lower continuity. INTERPRETATION: We found that continuity of physician care in the last year of life was low, especially in those with cancer. Further research is needed to validate measures of continuity against end-of-life health care outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".