Intensity of outpatient physician care in the last year of life: a population-based retrospective descriptive study
Bibliographic record
Abstract
BACKGROUND: For many patients, health care needs increase toward the end of life, but little is known about the extent of outpatient physician care during that time. The objective of this study was to describe the volume and mix of outpatient physician care over the last 12 months of life among patients dying with different end-of-life trajectories. METHODS: We conducted a retrospective descriptive study involving adults (aged ≥ 18 yr) who died in Ontario between 2013 and 2017, using linked provincial health administrative databases. Decedents were grouped into 5 mutually exclusive end-of-life trajectories (terminal illness, organ failure, frailty, sudden death and other). Over the last 12 months and 3 months of life, we examined the number of physician encounters, the number of unique physician specialties involved per patient and specialty of physician, the number of unique physicians involved per patient, the 5 most frequent types of specialties involved and the number of encounters that took place in the home; these patterns were examined by trajectory. RESULTS: = 359 559) had a median age of 78 (interquartile range 66-86) years. The mean number of outpatient physician encounters over the last year of life was 16.8 (standard deviation [SD] 13.7), of which 9.0 (SD 9.2) encounters were with family physicians. The mean number of encounters ranged from 11.6 (SD 10.4) in the frailty trajectory to 24.2 (SD 15.0) in the terminal illness trajectory across 3.1 (SD 2.0) to 4.9 (SD 2.1) unique specialties, respectively. In the last 3 months of life, the mean number of physician encounters was 6.8 (SD 6.4); a mean of 4.1 (SD 5.4) of these were with family physicians. INTERPRETATION: Multiple physicians are involved in outpatient care in the last 12 months of life for all end-of-life trajectories, with family physicians as the predominant specialty. Those who plan health care models of the end of life should consider support for family physicians as coordinators of patient care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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".