A Survey of Practice Patterns for Rehabilitation Post Elbow Fracture
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Elbow fractures amount to 4.3% of all the fractures. The elbow is prone to stiffness after injury and fractures can often lead to significant functional impairment. Rehabilitation is commonly used to restore range of motion (ROM) and function. Practice patterns in elbow fracture rehabilitation have not been defined. The purpose of this study was to describe current elbow fracture rehabilitation practices; and compare those to the existing evidence base. METHODS: Hand therapists (n=315) from the USA (92%) and Canada (8%) completed a web-based survey on their practice patterns and beliefs related to the acute (0-6 weeks) and functional (6-12 weeks) phases of elbow fracture rehabilitation. RESULTS: More than 99% of respondents agreed that fracture severity, co-morbidities, time since fracture, compliance with an exercise program, psychological factors, and occupational demands are important prognostic indicators for optimal function. Strong agreement was found with the use of patient education (95%) and active ROM (86%) in the acute stage while, home exercise programs (99%), active ROM (99%), stretching (97%), strengthening (97%), functional activities (ADLs and routine tasks) (97%), passive ROM (95%), and active assisted ROM (95%) were generally used in the functional stage. The most commonly used impairment measures were goniometry (99%), Jamar dynamometry (97%), and hand held dynamometry (97%). Agreement on the use of patient-reported outcome measures was very minimal (1.3%- 35.6%). CONCLUSIONS: Exercise, education, and functional activity have high consensus as components of elbo fracture rehabilitation. Future research should focus on defining the optimal dosage and type of exercise/activity, and establish core measures to monitor outcomes of these interventions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".