Lung Transplant Pulmonologists' Views of Specialty Palliative Care for Lung Transplant Recipients
Bibliographic record
Abstract
Background: Lung transplant recipients with serious illness may benefit from but rarely receive specialty palliative care (SPC) services. Transplant pulmonologists' views of SPC may be key to understanding SPC utilization but have not been well characterized. Objectives: (1) To understand how transplant pulmonologists view SPC and decide to refer transplant recipients and (2) to identify unique aspects of lung transplantation that may influence referral decisions. Design: We conducted semistructured interviews with transplant pulmonologists at nine geographically diverse high-volume North American transplant centers with SPC services. A multidisciplinary team analyzed interview transcripts using constant comparative methods to inductively develop and refine a coding framework related to SPC views and referral decisions. Results: We interviewed 38 transplant pulmonologists; most (36/38) had referred lung transplant recipients to SPC. Participants described SPC as a medical specialty that aims to improve quality of life and distinguished SPC from hospice care, which was considered end-of-life care. Participants who viewed transplant as a temporary solution ( n = 17/38, 45%) described earlier utilization of SPC alongside disease-directed therapies, whereas those who viewed transplant as survival-focused ( n = 21/38, 55%) described utilization of SPC after disease-directed therapies were exhausted. Concerns about one-year survival metrics and use of addicting medications for symptom palliation were barriers to referral. Conclusions: Transplant pulmonologists' SPC referral practices may be related to their views of lung transplantation. Optimizing use of SPC in lung transplantation will require improving communication between transplant pulmonology and SPC to ensure a collaborative effort toward patient-centered goals while addressing unique barriers to SPC referral.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".