Outcomes of lung transplant candidates referred for co-management by palliative care: A retrospective case series
Bibliographic record
Abstract
BACKGROUND: Lung transplant candidates experience important symptoms, but they are rarely referred for palliative care consultation until they are deemed ineligible for transplant. Our lung transplant service has a high rate of palliative care referral for patients awaiting transplant. AIM: We reviewed the characteristics, interventions, and outcomes of lung transplant candidates referred for co-management by palliative care, to determine whether they safely received opioids and went on to transplantation. DESIGN AND PARTICIPANTS: Retrospective review of lung transplant candidates referred to our palliative care consultation service between January 2010 and May 2012. RESULTS: Of 308 lung transplant candidates, 64 (20.7%) were referred to palliative care. Most had interstitial lung disease and were referred for dyspnea and a rapidly deteriorating course. A total of 59 (92%) were prescribed opioids for dyspnea, 55/59 used the opioids more than once, and 38/59 were maintained on standing opioids. There were no episodes of clinically important opioid toxicity or respiratory depression, and there was a trend toward increased exertion during exercise sessions post-opioid versus pre-opioid (19.3 vs 17.0 kcal, respectively, p = 0.06). At last follow-up, 30 (47%) had been transplanted, 23 (36%) had died while on the wait-list, 9 (14%) had died after delisting, and 2 (3%) were still awaiting transplantation. Of the 30 patients who underwent lung transplantation, only 7 (23%) still required an opioid prescription 1 month post-discharge. CONCLUSION: In lung transplant candidates, palliative care and opioids in particular can be safely provided without compromising eligibility for transplantation. Palliative care should not be delayed until a patient is deemed ineligible for transplant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".