Enhancing Interpretability of Patient-Reported Outcomes Measurement Information System (PROMIS) and Related Measures in Rehabilitation Populations: A Systematic Review of Clinical and Research Applications
Bibliographic record
Abstract
OBJECTIVE: To evaluate and synthesize interpretability metrics, including minimal important change (MIC), minimal important difference (MID), and minimal detectable change (MDC), across Patient-Reported Outcomes Measurement Information System (PROMIS) and related systems (Quality of Life in Neurological Disorders [Neuro-QoL], Quality of Life in Traumatic Brain Injury [TBI-QoL], Quality of Life in Spinal Cord Injury [SCI-QoL]) in rehabilitation populations. DATA SOURCES: Comprehensive searches of electronic databases (MEDLINE, EMBASE, PsycINFO, HaPI, CINAHL, Cochrane Library, Web of Science) and clinical trial registries (ISRCTN Registry, ClinicalTrials.gov) were conducted from inception through March 23, 2024, in consultation with an information specialist. STUDY SELECTION: Eligible studies assessed interpretability metrics in rehabilitation populations using PROMIS, Neuro-QoL, TBI-QoL, or SCI-QoL. Studies of pediatric or nonrehabilitation populations, abstracts, posters, or consensus statements were excluded. A total of 202 studies met inclusion criteria. DATA EXTRACTION: Two independent reviewers extracted study characteristics, interpretability metrics, and analytical methods following COnsensus-based Standards for the Selection of Health Measurement Instruments guidelines. DATA SYNTHESIS: MIC, MID, and MDC values varied widely across populations and domains. PROMIS mental health domains (eg, depression, anxiety, fatigue) demonstrated relatively consistent estimates, whereas physical function domains were more variable, particularly in chronic and geriatric groups. PROMIS Computer Adaptive Testing measures showed fewer floor and ceiling effects than short forms, indicating enhanced sensitivity to change. Limited data were available for SCI-QoL and TBI-QoL. CONCLUSIONS: Standardizing interpretability metrics and expanding research on SCI-QoL and TBI-QoL are critical to improving the clinical utility of these measures in rehabilitation. Future work should incorporate response-shift considerations and establish population-specific cut-points to support patient-centered care and evidence-based practice.
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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 | 0.211 | 0.479 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.028 | 0.023 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".