Effects of Gender, Age, and Time on Wrist Pain up to Two Years Following Distal Radius Fracture
Bibliographic record
Abstract
© 2020 by Begell House, Inc. www.begellhouse.com. Distal radius fractures (DRFs) represent the highest incidence of orthopedic frac-tures. The pattern of pain recovery following injury is an important clinical benchmark, as is understanding whether age or gender influences the process. The purpose of this study is to determine whether age or gender influences pain in individuals after a DRF for up to 2 yr postfracture. We use a mixed hierarchal analysis to determine whether differences resulting from age and/or gender occur in the pain subscale of a patient-rated wrist evaluation. At 2, 3, 6, 12, and 24 mo after surgical or nonsurgical fracture, we analyzed 1508 participants with a mean age of 53.5 yr (standard deviation = 16.3). The majority were female (70%), and most had injured their left hand (52.9%). Pain scores progressively improved, but we found no statistical differences in pain by age or gender. However, the following categories showed more statistically significant DRFs: ages 51–66 (Z = 2.83; 95% confidence interval [CI] = 1.6 to 9.1; p = 0.05) and female gender (Z = 2.84; 95% CI = 1.7 to 9.3; p = 0.05). Increased DRFs occurred in females aged 51–65, but they experienced lower levels of pain than their male counterparts. Pain slowly decreased over time, with significant pain reductions at 6 and 12 mo postfracture.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".