Validation of PROMIS Global-10 compared with legacy instruments in patients with shoulder instability
Bibliographic record
Abstract
Background Outcomes instruments are used to measure patients’ subjective assessment of health status. The Patient-Reported Outcomes Measurement Information System (PROMIS) Global-10 was developed to be a concise yet comprehensive instrument that provides physical and mental health scores and an estimated EuroQol-5 Dimension (EQ-5D) score. Methods A total of 175 prospectively enrolled patients with shoulder instability completed the PROMIS Global-10, EQ-5D, American Shoulder and Elbow Surgeons (ASES), Single Assessment Numeric Evaluation (SANE), and Western Ontario Shoulder Instability Index. Spearman correlations between PROMIS scores and the legacy instruments were calculated. Bland–Altman analysis assessed agreement between estimated and actual EQ-5D scores. Floor and ceiling effects were recorded. Results Correlation between actual and estimated EQ-5D was excellent-good (0.64/ p < 0.0005), but Bland–Altman agreement revealed high variability for estimated EQ-5D scores (95% CI: −0.30 to +0.34). Correlation of PROMIS physical scores was excellent-good with ASES (0.69/ p < 0.0005), good with SANE (0.43/ p<0.0005), and poor with WOSI (0.17/ p = 0.13). Correlation between PROMIS mental scores and all legacy instruments was poor. Conclusions PROMIS Global-10 physical function scores show high correlation with ASES but poor correlation with other legacy instruments, suggesting it is an unreliable outcomes instrument in populations with shoulder instability. The PROMIS Global-10 cannot replace actual EQ-5D scores for cost-effectiveness assessment in this population. Level of evidence Level II, study of diagnostic test.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".