The Use of Patient-reported Outcomes (PRO) Within Comparative Effectiveness Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The goal of comparative effectiveness research (CER) is to explain the differential benefits and harms of alternate methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care. To inform decision making, information from the patient's perspective that reflects outcomes that patients care about are needed and can be collected rigorously using appropriate patient-reported outcomes (PRO). It can be challenging to select the most appropriate PRO measure given the proliferation of such questionnaires over the past 20 years. OBJECTIVE: In this paper, we discuss the value of PROs within CER, types of measures that are likely to be useful in the CER context, PRO instrument selection, and key challenges associated with using PROs in CER. METHODS: We delineate important considerations for defining the CER context, selecting the appropriate measures, and for the analysis and interpretation of PRO data. Emerging changes that may facilitate CER using PROs as an outcome are also reviewed including implementation of electronic and personal health records, hospital and population-based registries, and the use of PROs in national monitoring initiatives. The potential benefits of linking the information derived from PRO endpoints in CER to decision making is also reviewed. CONCLUSIONS: The recommendations presented for incorporating PROs in CER are intended to provide a guide to researchers, clinicians, and policy makers to ensure that information derived from PROs is applicable and interpretable for a given CER context. In turn, CER will provide information that is necessary for clinicians, patients, and families to make informed care decisions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it