Comparison of Palliative Care Models in Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
Introduction: Palliative care (PC) is recommended in idiopathic pulmonary fibrosis (IPF) patients but poorly implemented. Integration of PC into routine management by pulmonologists may improve overall and end-of-life (EOL) care, but the optimal model of PC delivery is unknown. Objective: To describe three PC care delivery models and their impact on EOL; the Multidisciplinary Collaborative ILD clinic, Edmonton, Canada (EC) and the Bristol ILD Service, UK (BC) that provide primary level PC; and the Queen’s University ILD Clinic, Kingston, Canada (QC), which refers IPF patients to a specialist PC Clinic using specific referral criteria. Methods: A multicenter retrospective observational study of IPF patients receiving care in the identified clinics (2012–2018) was designed. Demographics; PC delivery, including symptom management; advance care planning (ACP); and location of death data were examined. Results: 298 IPF patients were included (EC 95, BC 84, and QC 119). Median age was 71 years with 74% males. Overall, 63% (188) patients received PC. Primary PC approach in EC and BC led to more patients receiving PC (98% EC, 94% BC and 13% QC (p < 0.001/<0.001)) with earlier initiation compared to QC. Associated higher rates of non-pharmacologic dyspnea management [98% EC, 94% BC, and 2% QC (p < 0.001/<0.001); opioids (45% EC and BC, and 23% QC (p < 0.001/<0.001)); and ACP (100% EC and BC, and 13% QC patients (p < 0.001/<0.001))] were observed. Median follow up (IQR) was 16 months (5–28) with 122 deaths (41%). Primary PC model in EC and BC decedents was associated with more PC delivery (91% EC, 92% BC and 19% QC (p < 0.001)) with more symptoms management, oxygen, and opiate use than QC (p < 0.001; p = 0.04; p = 0.01). EOL discussions occurred in 73% EC, 63% BC, and 4% QC decedents (p = 0.001). Fifty-nine% (57) died at home or hospice and 38% (36) in hospitals. Concordance rate between preferred and actual location of death was 58% in EC (0.29 (−0.02–0.51)) and 37% in BC models (−0.11 (−0.20–0.15)). Conclusions: Primary PC approach for IPF is feasible in ILD clinics with concurrent disease management and can improve access to symptom management, ACP, PC and EOL care. Reliance on PC specialist referral for PC initiation outside of the ILD clinic can result in delayed 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".