Patient-reported dyspnea and health predict waitlist mortality in patients waiting for lung transplantation in Japan
Bibliographic record
Abstract
Abstract Background Waitlist mortality due to donor shortage for lung transplantation is a serious problem worldwide. Currently, the selection of recipients is mainly based on registration order in Japan. However, scientific evidence for risk stratification for waitlist mortality is needed in future. We hypothesized that patient-reported dyspnea and health would predict mortality in patients waitlisted for lung transplantation. Methods Using data on 203 patients who were registered as candidates for lung transplantation from deceased donors, we analyzed factors related to waitlist mortality. Dyspnea was evaluated by the modified Medical Research Council (mMRC) dyspnea scale and health status was measured with the St. George’s Respiratory Questionnaire (SGRQ). Results Among 197 patients who met inclusion criteria, the main underlying disease was interstitial pneumonia (IP) in 99 patients. During the median follow-up period of 572 days, 72 patients on the waitlist died and 96 received lung transplantation (69 from deceased donor). Univariable competing risk analyses revealed that both mMRC dyspnea and SGRQ Total were significantly associated with waitlist mortality (p = 0.003 and p < 0.001). Multivariable competing risk analyses revealed that the mMRC and SGRQ were associated with waitlist mortality, among age, IP, arterial carbon dioxide pressure, and forced vital capacity, which were all significant factors in univariable analyses. Conclusions Both mMRC dyspnea and SGRQ were significantly associated with waitlist mortality regardless of patients’ background, underlying disease, and pulmonary function. Patient-reported dyspnea and health should be measured not only from the perspective of multi-dimensional analysis including subjective perceptions, but also as risk stratification for waitlist mortality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".