Development and Evaluation of the Boston University Osteoarthritis Functional Pain Short Form (BU-OA-FPS)
Bibliographic record
Abstract
Background: Pragmatic studies have gained popularity, thus emphasizing the need for patient-reported outcomes (PRO) to be integrated into electronic health records. Objective: This study describes the development of a customized short form from the Boston University Osteoarthritis Functional Assessment PRO (BU-OA-PRO) for a specific pragmatic clinical trial. Methods: A Functional Pain Short Form was created from an existing item bank of deidentified data in the BU-OA-PRO. Item response theory (IRT) methods were used to select items. Reliability was measured with the Cronbach alpha, then with IRT simulation methods. To examine validity, ceiling and floor effects, correlations between the short-form scores and scores from the BU-OA-PRO and the Western Ontario McMasters University Osteoarthritis Index (WOMAC) Pain and Difficulty subscales, and the area under the curve (AUC) were calculated. A minimum detectable change at 90% confidence (MDC90) was calculated based on a calibration sample. Results: The BU-OA-PRO was reduced from 126 items to 10 items to create the BU-OA Functional Pain Short Form (BU-OA-FPS). The Cronbach alpha indicated high internal consistency (0.91), and reliability distribution estimates were 0.96 (uniform) and 0.92 (normal). Low ceiling effects (4.57%) and floor effects (0%) were found. Moderate-to-high correlations between the BU-OA-PRO and BU-OA-FPS were found with WOMAC Pain (BU-OA-FPS = 0.67; BU-OA-PRO = 0.64) and Difficulty (BU-OA-FPS = 0.73; BU-OA-PRO = 0.69) subscales. The correlation between the BU-OA-PRO and BU-OA-FPS was 0.94. The AUC ranged from 0.80 to 0.88. The MDC90 was approximately 6 standardized points. Conclusions: The BU-OA-FPS provides reliable and valid measurement of functional pain. Pragmatic studies may consider the BU-OA-FPS for use in electronic health records to capture outcomes.
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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 | 0.044 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".