Do Impairments Predict Hand Dexterity After Distal Radius Fractures? A 6-Month Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The relationship of routinely measured grip and motion measures may be related to hand dexterity. This has not yet been thoroughly examined following a distal radius fracture (DRF). The purpose of this study was to investigate if impairments in range of motion (ROM) and grip strength predict hand dexterity 6 months following a DRF. METHODS: Patients with DRFs were recruited from a specialized hand clinic. Hand grip was assessed with a J-Tech dynamometer; ROM was measured using standard landmarks and a manual goniometer. Multiple regression analyses were performed to identify whether potential predictors (grip, ROM, age, hand dominance, and sex) were associated with 3-month or 6-month outcomes in large- and small-object subtests of the NK dexterity test in the affected hand. RESULTS: Age, sex, and arc motion for radial-ulnar deviation were significant predictors of large-object hand dexterity explaining the 23% of the variation. For small-object hand dexterity, age and flexion-extension arc motion were significant predictors explaining 11% of the variation at 3 month after the fracture (n = 391). At 6 months post injury (n = 319), grip strength, arc motion for flexion-extension, and age were found to be significant predictors of large-object dexterity explaining 34% of the variance. For the small objects, age, grip strength, sex, and arc motion of radial-ulnar deviation explained 25% of the variation. CONCLUSIONS: Although this confirms that the impairments in ROM and grip that occur after a DRF can explain almost one-third of the variation in hand dexterity, it also suggests the need for dexterity testing to provide more accurate assessment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".