The Impact of Integrated Palliative Care on Survival in Idiopathic Pulmonary Fibrosis: A Retrospective Multicenter Comparison
Bibliographic record
Abstract
Background: Early and integrated palliative care is recommended for patients with idiopathic pulmonary fibrosis. Unfortunately, palliative care delivery remains poor due to various barriers in practice. This study describes various palliative care delivery models in a real-world cohort of patients with idiopathic pulmonary fibrosis, examines the predictors of survival in this cohort of patients, and explores the impact of palliative care on survival. Design: Charts were reviewed retrospectively and analyzed. The primary outcome was survival during a 4-year follow-up period. Two multivariable models were created to examine the impact of therapeutic strategies including palliative intervention on survival. Results: 298 patients with idiopathic pulmonary fibrosis were enrolled from 3 interstitial lung disease clinics with different palliative care models in Edmonton, Canada; Bristol, UK; and Kingston, Canada. 200 (67%) patients received palliative care and 119 (40%) died during follow up. Primary palliative care models (Edmonton and Bristol) delivered palliative care to 96% and 100% respectively compared 21% in the referral model (Queens). Palliative care [adjusted hazard ratio (aHR) .28 (.12-.65)] along with the use of antifibrotics [aHR .56 (.37-.84)], and body mass index >30 [aHR .47 (.37-.85)] reduced the risk of death in our idiopathic pulmonary fibrosis cohort. Opioid use was associated with worse survival [aHR 2.11 (1.30-23.43)]. Conclusions: Both palliative care and antifibrotic use were associated with survival benefit in this cohort of patients with idiopathic pulmonary fibrosis after adjusting for covariates. The benefit was seen despite differences in disease severity and different palliative care delivery models.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".