Baseline Predictors of Pain and Disability One Year following Extra-Articular Distal Radius Fractures
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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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 | 0.001 | 0.004 |
| 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 | 0.001 | 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".