Baseline Predictors of Pain and Disability One Year following Extra-Articular Distal Radius Fractures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Distal radius fractures are common injuries; however, identifying which factors are responsible for predicting outcomes remains an area of controversy. The purpose of this study was to define factors predictive of patient-reported pain and disability at 1 year in a prospective cohort of extra-articular distal radius fractures (n = 222). Data were collected at the initial visit and after 3, 6, and 12 months. The primary outcome was the 1-year patient-rated wrist evaluation (PRWE) score. The effect of baseline patient and injury characteristics on the 1-year PRWE score was assessed. Univariate and forward stepwise regression analyses both agreed that the most influential predictor of pain and disability at 1 year was injury compensation. The 1-year PRWE score was significantly higher for subjects involved with third-party claims (35.48) compared to those that were not involved in any claims (14.97), p = 0.006. The regression model found that three baseline factors - injury compensation, education, and other medical comorbidities - explained 16.4% of the variance in PRWE scores at 1 year. No injury characteristic, including the degree of initial fracture displacement, was found to significantly influence the 1-year PRWE score. This study has shown that baseline patient and injury characteristics play a small role in predicting 1-year patient-reported pain and disability in extra-articular distal radius fractures. Conceptual factors outside of this biomedical model should be investigated.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it