Translating the Science of Patient-Reported Outcomes Assessment Into Clinical Practice
Bibliographic record
Abstract
Patient-reported outcomes (PROs) are based on direct reporting by patients without the intervention of an observer. They include the self-assessment of functional status, symptoms, and other concerns such as needs and satisfaction with care. Health-related quality of life (HRQOL) assessment is a form of PRO and often includes both functional status and symptoms. The science underlying the assessment of HRQOL in clinical practice requires an understanding of the relationships between symptoms, functional status, and HRQOL, as well as instrument selection, and analysis and interpretation of the data. A modification of the Wilson and Cleary model is proposed to show the likelihood of bidirectional relationships between symptoms, functions, and HRQOL. Instrument selection should be based on the measurement properties of the instruments and patient populations in which they will be used. Analyses of data that allow a calculation of the proportion of patients who benefit from an intervention are preferred to analyses that show only the mean change in scores from baseline. HRQOL assessment in clinical practice has been shown to lead to a better understanding of patients' concerns with improvement in counseling and referral for required services. Potentially, HRQOL assessment should also be used to monitor the progress of a patient's disease and benefit from treatment.
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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 | 0.072 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".