Predictors of Changes in Health Status Between and Within Patients 12 Months Post Left Ventricular Assist Device Implantation
Bibliographic record
Abstract
BACKGROUND: Improving patient-reported outcomes (e.g. health status) has become an important goal in left ventricular assist device (LVAD) therapy, in addition to reducing mortality and morbidity. We examined predictors of changes in health status scores between and within patients 12 months post LVAD implantation. METHODS: Health status [Kansas City Cardiomyopathy Questionnaire (KCCQ); Short-Form 12 (SF-12)] were assessed at 3-4 weeks after implantation, and at 3, 6 and 12 months follow up in 54 LVAD patients (74% men; mean age 54 ± 9 years). RESULTS: Patients experienced significant improvements in health status between baseline and 3 months follow-up as assessed by the KCCQ (clinical summary score: F = 33.49, P < 0.001; overall summary score: F = 31.13, P < 0.001) and the SF-12 (physical component score: F = 31.59, P < 0.001; mental component score: F = 21.77, P < 0.001), but not between 3 months and 12 months follow-up (P > 0.05 for all). Higher scores on anxiety and depression over time, older age, lower ejection fraction, and more co-morbidity were associated with poorer health status scores on one or both of the KCCQ and SF-12 subscales. The majority of the between-patient variance of the mental component summary scores (82.6%), but not the KCCQ overall summary score (41.9%), KCCQ clinical summary score (36.2%) and physical component summary scores (23.2%), was explained by the sociodemographic, clinical and psychological factors. CONCLUSION: The majority of LVAD patients show a significant improvement in health status after LVAD implantation. However, there are large differences in individual health status score trajectories which are only partly explained by measures of disease severity pre-LVAD, co-morbidity and psychological stress.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".