Measuring health-related quality of life in patients with peripheral vascular malformations with PROMIS® computerized adaptive tests
Bibliographic record
Abstract
BACKGROUND: The Patient-Reported Outcomes Measurement Information System (PROMIS) computerized adaptive tests (CATs) have been recommended for measuring health-related quality of life (HRQoL) in rare diseases. OBJECTIVE: To assess whether PROMIS CATs reliably measure HRQoL core outcome domains in adults with vascular malformations, and if so, to establish reference values for this population. METHODS: In this prospective cohort study, adult patients with vascular malformations at our tertiary referral center were invited to complete PROMIS CATs measuring HRQoL core outcome domains, followed by a retest seven days after completing the first questionnaires. Patients completed PROMIS CATs on pain interference, physical function, anxiety, depression, ability to participate in social roles and activities, and the PROMIS numeric rating scale (NRS) on pain intensity. Test-retest reliability was quantified by calculating intraclass correlation coefficients (ICC), standard errors of measurement (SEM) and smallest detectable changes (SDC). An ICC ≥0.7 was considered adequate. Moreover, descriptive statistics for each CAT were calculated and compared against the Dutch general population. RESULTS: One-hundred-twenty-three patients completed at least one CAT and 55 patients completed the retest of at least one CAT. All ICCs were above 0.85, SEMs were under 5 points and SDCs were around 10 points, indicating adequate test-retest reliability and measurement errors. All average CAT scores of patients with vascular malformations were within normal limits compared to PROMIS reference values for the Dutch general population. CONCLUSION: PROMIS CATs reliably measure physical, mental and social HRQoL in adults with vascular malformations. Reference values for PROMIS CATs in this population were established.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".