Ultrasound-Guided Manipulation does not Prevent Malalignment Over Landmark-Based Fracture Reduction in Distal Radius Fracture (Colles)
Bibliographic record
Abstract
Introduction: This systematic review aims to determine the relative risk of distal radius (Colles) fracture (DRF) malalignment between ultrasound (USG)-guided and conventional/landmark guided/blind manipulation and reduction (M&R). Methods: We searched 3932 records from major electronic bibliographic databases on USG-guided manipulation of DRF. Studies with randomized, quasi-randomized, and cross-sectional study designs meeting the inclusion criteria were included in this review. USG and landmark-guided DRF manipulations were named cases and controls, respectively. The Newcastle–Ottawa Scale was used to assess the quality of included studies. Results: Thirteen and nine studies were analysed for qualitative and quantitative analysis in this review. Nine hundred fifty-one DRF patients (475 cases and 476 controls) from 9 studies with mean ages of 51.52 ± 11.86 (22–92) and 55.82 ± 11.28 (18–98) years for cases and controls were pooled for this review. The pooled relative risk estimate from the studies included in the meta-analysis was 0.90 (0.74–1.09). There was a 10% decrease in the risk of malalignment with USG than the landmark guided M&R of DRF. The I 2 statistic estimated a heterogeneity of 83%. Sensitivity analysis revealed a relative risk of 1.00 (0.96–1.05). Conclusion: The USG-guided manipulation does not prevent malalignment over the landmark-based manipulation of DRF. The risk of bias across the included studies and heterogeneity of 83% mandates further unbiased, high-quality studies to verify the findings of this review.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".