Embedded Specialist Palliative Care in Cystic Fibrosis: Results of a Randomized Feasibility Clinical Trial
Bibliographic record
Abstract
Background: Cystic fibrosis (CF) is a progressive genetic disease characterized by multisystem symptom burden. Specialist palliative care (PC), as a model of care, has been shown to be effective in improving quality of life and reducing symptom burden in other conditions, but has not been tested in CF. Objectives: To develop and test the feasibility and acceptability of a specialist PC intervention embedded within an outpatient CF clinic. Design: Single-site, equal-allocation randomized pilot study comparing usual care with addition of four protocolized quarterly visits with a PC nurse practitioner. Participants: Adults with CF age ≥18 years with any of the following: FEV1% predicted ≤50, ≥2 CF-related hospitalizations in the past 12 months, supplemental oxygen use, or noninvasive mechanical ventilation use, and moderate-or-greater severity of any symptoms on the Edmonton Symptom Assessment Scale. Measurements: Randomization rate, intervention visit completion, data completements, participant ratings of intervention acceptability and benefit, and intervention delivery fidelity. Results: We randomized 50 adults with CF of 65 approached (77% randomization rate) to intervention (n = 25) or usual care (n = 25), mean age 38, baseline mean FEV1% predicted 41.8 (usual care), and 41.2 (intervention). No participants withdrew, five were lost to follow-up, and two died (88% retention). In the intervention group, 23 of 25 completed all study visits; 94% stated the intervention was not burdensome, and 97.6% would recommend the intervention to others with CF. More than 90% of study visits addressed topics prescribed by intervention manual. Conclusions: Adding specialist PC to standard clinic visits for adults with CF is feasible and acceptable.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".