Integrating Patients into Meaningful Real-World Research
Bibliographic record
Abstract
Research in respiratory, sleep, and critical care medicine has historically been the domain of scientists and clinicians attempting to understand pathophysiological mechanisms and consequences of disease in an effort to develop effective treatments. This traditional approach of placing scientific rigor before the patient's reality is changing. There is growing recognition of the importance of integrating patient perspectives (e.g., preferences, expectations, and expanded definitions of what constitutes "successful" outcomes) into clinical research to achieve meaningful results for a broader group of stakeholders. This evolution is reflected in the growth of patient-centered organizations and patient advocacy groups that seek to meaningfully integrate patients into the process of prioritizing research needs and creating alliances wherein patients and researchers can partner together to accomplish research goals. In tandem, a growing number of real-world trials (i.e., those with broader, more representative patient populations and routine care pathways) now complement findings from traditional randomized controlled trials and offer new opportunities to design studies that better reflect patients' healthcare preferences and experiences. Patients' perspectives are key determinants of treatment adherence and outcomes, as well as the feasibility and likely value of implementing care pathways. The advent of smartphone and push technologies offer new opportunities for the collection of more patient-centered and ecologically valid patient data, thereby adding new dimensions to meaningfully integrate patients into real-world research.
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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 | 0.399 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.036 | 0.034 |
| Open science | 0.006 | 0.047 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".