An investigation of the relationship between measures of pain intensity, pain affect, and disability, in patients with shoulder dysfunction
Bibliographic record
Abstract
OBJECTIVES: Numerous outcomes measures can be used to capture and differentiate change in different constructs comprising recovery. Consequently, patients are often burdened by completing a number of measures which involves considerable time and effort. The purpose of this longitudinal, observational study was to identify the number of dimensions in a battery of self-report findings in a patient population who received shoulder injections to investigate the association of the instruments. METHODS: Ninety-nine subjects, with diagnoses of adhesive capsulitis, labral injuries, rotator cuff injuries, and osteoarthritis completed outcomes measures including five different forms of pain intensity measures, the McGill Short Form Questionnaire, and the Disabilities of the Arm, Shoulder, and Hand Questionnaire. Change scores were calculated at 4 weeks and an exploratory factor analysis (EFA) with varimax rotation was used to analyze dimensionality. The relationship between the raw scores of the seven measures was investigated using a correlation matrix. RESULTS: The EFA yielded only one factor and the raw score correlations demonstrated very strong, significant associations. The finding of a single factor suggests that in this sample of patients, only one dimension of change, most likely a change in pain, is represented by the seven individual outcomes measures. DISCUSSION: In this isolated example, one outcomes measure would have been sufficient in determining outcome and could have reduced the administrative burden to the caregivers and the patients.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".