Getting Ready for Patient-Reported Outcomes Measures (PROMs) in Clinical Practice
Bibliographic record
Abstract
Patient-reported outcome measures (PROMs) include reports and ratings provided by patients or their proxies about their health, functioning, health behaviours and quality of care. PROMs reflect the patient perspective and increase the comprehensiveness of outcome measurement in clinical research. There is growing interest in using PROMs in clinical practice: for screening, monitoring and improving communication at the individual level; and to aid in decision-making, monitor populations and assess quality in the aggregate. For use in clinical practice, the authors draw an analogy to getting to the prom (a North American graduation dance). Whom to go with? They recommend seeking a group of partners and developing methods and standards with national and international groups. The authors advocate for incentives to encourage broad participation. What to wear? They suggest selecting existing, well-tested PROMs and highlight the ability of dynamic questionnaires to provide tailored assessments. How to get there? The authors recommend web-based formatting of measures and results, using their system, PatientViewpoint, as an example. How to get the most out of the experience? They discuss the variety of applications of PROMs data and recommend providing clinicians with actions that they can take to mitigate problems in non-clinical domains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.044 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".