Characteristics, Interventions, and Outcomes of Lung Transplant Recipients Co-Managed with Palliative Care
Bibliographic record
Abstract
BACKGROUND: Lung transplantation (LT) recipients carry a high symptom burden. Palliative Care (PC) is a field of medicine focused on symptom control and psychosocial support, but transplant recipients are often referred to PC very late in the disease course, if at all. In our institution, the LT service has increasingly consulted PC to co-manage LT recipients with end-stage graft dysfunction or other terminal conditions. We present the characteristics, PC interventions used, and outcomes of these patients. METHODS: We conducted a single-center, retrospective, cohort study of LT recipients referred for PC consultation between January 2010 and May 2012. We collected patient demographics, timing and location of PC consultation, PC interventions, and patient outcomes. RESULTS: Twenty-four patients met the inclusion criteria. Sixteen (67%) had chronic allograft dysfunction. Reasons for referral were dyspnea (42%), end-of-life planning (42%), pain (29%), cough (4%), anxiety (4%), and depression (4%). Referral was made a median of 3.2 (range, 0.2 to 18) years from transplant and a median 14 days (range, 0 to 227 days) from death. Eighty-three percent of consultations occurred >48 hours from time of death. Ninety-two percent of patients were prescribed opioids over their course of treatment. Among the 12 (50%) who died in our center, 10 (83%) were receiving comfort medications. Eight patients (33%) initially requested full resuscitation at the time of PC consultation, but seven of these patients (or their surrogates) later agreed to a do not resuscitate (DNR) order; the eighth was still alive at last follow-up. No patient in this study received cardiopulmonary resuscitation (CPR) at the time of death. CONCLUSION: LT recipients referred for PC co-management typically receive comfort medications and avoid the aggressive end-of-life care usually reported for this population. The effect of PC interventions on patient quality of life requires further study.
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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".