Patient-reported outcome 2 years after lung transplantation: does the underlying diagnosis matter?
Bibliographic record
Abstract
PURPOSE: Transplantation has the potential to produce profound effects on survival and health-related quality of life (HRQL). The inclusion of the patient's perspective may play an important role in the assessment of the effectiveness of lung transplantation. Patient perspectives are assessed by patient-reported outcome measures, including HRQL measures. We describe how patients' HRQL among different diagnosis groups can be used by clinicians to monitor and evaluate the outcomes associated with transplantation. METHODS: Consecutive lung transplant recipients attending the lung transplant outpatient clinic in a tertiary institution completed the 15-item Health Utilities Index (HUI) questionnaire on a touchscreen computer. The results were available to clinicians at every patient visit. The HUI3 covers a range of severity and comorbidities in eight dimensions of health status. Overall HUI3 scores are on a scale in which dead = 0.00 and perfect health = 1.00; disability categories range from no disability = 1 to severe disability <0.70. Single-attribute and overall HUI3 scores were used to compare patients' HRQL among different diagnosis groups. Random-effect models with time since transplant as a random variable and age, gender, underlying diagnoses, infections, and broncholitis obliterans syndrome as fixed variables were built to identify determinants of health status at 2-years posttransplantation. RESULTS: Two hundred and fourteen lung transplant recipients of whom 61% were male with a mean age of 52 (19-75) years were included in the study. Chronic obstructive pulmonary disease and cystic fibrosis patients displayed moderate disability, while pulmonary fibrosis and pulmonary arterial hypertension patients displayed severe disability. Patients with chronic obstructive pulmonary disease had the worst pain level, whereas patients with pulmonary fibrosis had the worst emotion and cognition levels. A random-effect model confirmed that development of broncholitis obliterans syndrome was the most important determinant of health status (P = 0.03) compared to other variables, such as cytomegalovirus infections and underlying diagnoses. CONCLUSION: Descriptions of patients' HRQL among different diagnosis groups could be used by clinicians to assist individualized patient care.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".