Change in patient-reported outcomes after cardioverter-defibrillator implantation
Bibliographic record
Abstract
Some people, because they have a genetic predisposition or heart disease, are at high risk for cardiac arrhythmias that could cause their hearts to stop. The implantable cardioverter-defibrillator (ICD) is an effective therapy that recognises abnormal heart beats, can administer an electrical shock to stop a potentially lethal heart rhythm, and affords protection from the devastating consequences of sudden cardiac arrest. Patient-reported outcomes (PROs) are assessments provided directly by patients about various aspects of their health and quality of life. We sought to study the change in PROs after ICD implantation to identify people’s patterns of change, explore individual trajectories of change, and identify predictors of differences in individuals’ trajectories. The study was grounded in the Wilson and Cleary (1995) conceptual framework of quality of life and informed by the Patient-Reported Outcomes Measurement Information System domain framework. Using a prospective, longitudinal study design, data were obtained from 171 people undergoing ICD implantation at quaternary centres in British Columbia, Canada (55.5% response rate). PRO assessments were obtained immediately before implantation and at one, two, and six months following implantation. We employed individual growth modelling to analyse change within and between people. The participants had different physical, mental, and social health status PROs at baseline and, on average, demonstrated improvement. At most of the measurement occasions, the participants’ PROs remained poorer than those of average adult, urban-dwelling Canadians. There was significant individual variability in most of the trajectories, especially in the social functioning domains. Relative to men, women reported worse PROs initially (the relative mean difference in men’s and women’s scores ranged from 4.5% to 24.7% for 6 of the 12 indicators). Yet, the women’s rates of improvement were significantly faster than those of men. Women equalled or exceeded the men’s PROs at the six-month assessment (the relative mean difference ranged from 4.5% to 10.4%, depending on the PRO). Further research is needed to explore the individual change trajectories identified in this study, especially for those patients who did not improve over time, fully test the conceptual model that framed the research, and evaluate interventions aimed at improving PROs after ICD implantation.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".