A PRO-cision Medicine Methods Toolkit to Address the Challenges of Personalizing Cancer Care Using Patient-Reported Outcomes
Bibliographic record
Abstract
Patients are increasingly being asked to complete standardized, validated questionnaires with regard to their symptoms, functioning, and well-being [ie, patient-reported outcomes (PROs)] as part of routine care. These PROs can be used to inform patients' care and management, which we refer to as "PRO-cision Medicine." For PRO-cision Medicine to be most effective, clinicians and patients need to be able to understand what the PRO scores mean and how to act on the PRO results. The papers in this supplement to Medical Care describe various methods that have been used to address these issues. Specifically, the supplement includes 14 papers: 6 describe different methods for interpreting PROs and 8 describe how different PRO systems have addressed interpreting PRO scores and/or acting on PRO results. As such, this "Methods Toolkit" can inform clinicians and researchers aiming to implement routine PRO reporting into clinical practice by providing methodological fundamentals and real-world examples to promote personalized patient care.
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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 | 0.122 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.017 |
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".