A multi-level approach for the use of routinely collected patient-reported outcome measures (PROMs) data in healthcare systems
Bibliographic record
Abstract
Patient-reported outcome measures (PROMs) are instruments that measure health, including symptoms, functional status, health perceptions and health-related quality of life, as reported by patients themselves [ 1 ]. Today they gain importance in informing patients, caregivers, managers, and the public on the degree to which the goals of health care are attained. The introduction of PROMs into healthcare settings is not intended to replace existing outcome measures, but rather enhances health outcome measurement by making it more comprehensive and patient-centered. As such, PROMs data should be viewed as complimentary to various types of data traditionally collected in healthcare systems. The use of PROMs alongside clinician-reported outcomes and administrative data can better inform clinical and policy decisions. This underscores the importance of integrating PROMs into existing outcomes measurement systems and linking PROMs data with other patient data sources.
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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 | 0.192 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".