Effect of Primary Care Involvement on End‐of‐Life Care Outcomes: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To investigate the relationship between primary care involvement in end-of-life (EOL) care and health and utilization outcomes. DESIGN: Systematic review using MEDLINE and Web of Science. SETTING: All English literature published between 1994 and August 31, 2014, that included terms related to primary care providers (PCPs), continuity of care, EOL care, and palliative care. PARTICIPANTS: Individuals receiving care from a PCP at the end of life. MEASUREMENTS: Study design, subject characteristics, study outcomes and results. RESULTS: Of 2,812 studies screened, 13 were included in this study. The studies were mostly conducted in the United States (n = 5) and Canada (n = 4) and analyzed data collected from 1989 to 2010. Almost all studies used different definitions of PCP involvement in care, but in general, individuals who received more care from PCPs were more likely to be discharged or die with supportive care (home or hospice) than those receiving less PCP care. A few studies indicated that individuals seeing a PCP were less likely to have hospital or emergency department admissions, although the evidence for this was mixed. Studies linking PCP involvement to resource use, symptom management, and survival had mixed results or showed no association. CONCLUSION: When PCPs are involved in EOL care, people are more likely to die out of the hospital. Thus, the relationship with the PCP may be particularly important in EOL care, because PCPs may help individual establish goals of care and determine treatment preferences.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".