Estimation of a Preference-Based Summary Score for the Patient-Reported Outcomes Measurement Information System: The PROMIS <sup>®</sup> -Preference (PROPr) Scoring System
Bibliographic record
Abstract
BACKGROUND: ) consists of patient-reported outcome measures developed using item response theory. PROMIS is in need of a direct preference-based scoring system for assigning values to health states. OBJECTIVE: To produce societal preference-based scores for 7 PROMIS domains: Cognitive Function-Abilities, Depression, Fatigue, Pain Interference, Physical Function, Sleep Disturbance, and Ability to Participate in Social Roles and Activities. SETTING: Online survey of a US nationally representative sample ( n = 983). METHODS: Preferences for PROMIS health states were elicited with the standard gamble to obtain both single-attribute scoring functions for each of the 7 PROMIS domains and a multiplicative multiattribute utility (scoring) function. RESULTS: The 7 single-attribute scoring functions were fit using isotonic regression with linear interpolation. The multiplicative multiattribute summary function estimates utilities for PROMIS multiattribute health states on a scale where 0 is the utility of being dead and 1 the utility of "full health." The lowest possible score is -0.022 (for a state viewed as worse than dead), and the highest possible score is 1. LIMITATIONS: The online survey systematically excludes some subgroups, such as the visually impaired and illiterate. CONCLUSIONS: A generic societal preference-based scoring system is now available for all studies using these 7 PROMIS health domains.
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".