The importance of patient-reported outcomes: a call for their comprehensive integration in cardiovascular clinical trials
Bibliographic record
Abstract
Patient-reported outcomes (PROs), such as symptoms, health-related quality of life (HRQOL), or patient perceived health status, are reported directly by the patient and are powerful tools to inform patients, clinicians, and policy-makers about morbidity and 'patient suffering', especially in chronic diseases. Patient-reported outcomes provide information on the patient experience and can be the target of therapeutic intervention. Patient-reported outcomes can improve the quality of patient care by creating a holistic approach to clinical decision-making; however, PROs are not routinely used as key outcome measures in major cardiovascular clinical trials. Thus, limited information is available on the impact of cardiovascular therapeutics on PROs to guide patient-level clinical decision-making or policy-level decision-making. Cardiovascular clinical research should shift its focus to include PROs when evaluating the efficacy of therapeutic interventions, and PRO assessments should be scientifically rigorous. The European Society of Cardiology and other professional societies can take action to influence the uptake of PRO data in the research and clinical communities. This process of integrating PRO data into comprehensive efficacy evaluations will ultimately improve the quality of care for patients across the spectrum of cardiovascular disease.
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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 | 0.591 | 0.604 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.025 | 0.010 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".