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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".