Predictors of Specialty Outpatient Palliative Care Utilization Among Persons With Serious Illness
Bibliographic record
Abstract
CONTEXT: Outpatient Palliative Care (OPC) benefits persons living with serious illness, yet barriers exist in utilization. OBJECTIVES: To identify factors associated with OPC clinic utilization. METHODS: Emergency Medicine Palliative Care Access is a multicenter, randomized control trial comparing two models of palliative care for patients recruited from the Emergency Department (ED): nurse-led telephonic case management and OPC (one visit a month for six months). Patients were aged 50+ with advanced cancer or end-stage organ failure and recruited from 19 EDs. Using a mixed effects hurdle model, we analyzed patient, provider, clinic and healthcare system factors associated with OPC utilization. RESULTS: Among the 603 patients randomized to OPC, about half (53.6%) of patients attended at least one clinic visit. Those with less than high school education were less likely to attend an initial visit than those with a college degree or higher (aOR 0.44; CI 0.23, 0.85), as were patients who required considerable assistance (aOR 0.45; CI 0.25, 0.82) or had congestive heart failure only (aOR 0.46; CI 0.26, 0.81). Those with higher symptom burden had a higher attendance at the initial visit (aOR 1.05; CI 1.00, 1.10). Reduced follow up visit rates were demonstrated for those of older age (aRR 0.90; CI 0.82, 0.98), female sex (aRR 0.84; CI 0.71, 0.99), and those that were never married (aRR 0.62; CI 0.52, 0.87). CONCLUSION: Efforts to improve OPC utilization should focus on those with lower education, more functional limitations, older age, female sex, and those with less social support. Trial Registration ClinicalTrials.gov Identifier: NCT03325985.
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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 | 0.000 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".