Management of Very Distal Ulna Fractures: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To synthesize all-cause complications and reoperations data, and secondary clinical, functional, and radiographic outcomes after the management of very distal ulna fractures (VDUFs) either nonoperatively or operatively. DATA SOURCES: MEDLINE, Embase, and Web of Science were searched for English-language articles from inception to February 17, 2022. STUDY SELECTION: Studies reporting the nonoperative or operative management of VDUFs were eligible for inclusion. VDUFs were defined as either being Q2-Q5 distal ulna fractures using the OTA/AO Comprehensive Classification of Fractures for distal ulna fractures associated with distal radius fractures or being amenable to characterization by the classification system for ulnar head, neck and metaphyseal fractures by Biyani et al. DATA EXTRACTION: Two reviewers independently extracted data from included studies. Study validity was assessed using the methodological index for nonrandomized studies. DATA SYNTHESIS: Seventeen studies (512 VDUFs) were included for analysis. There were 209, 237, and 66 fractures in the nonoperative, open reduction internal fixation (ORIF), and distal ulna resection groups, respectively. Descriptive statistics including weighted mean values, standard deviations, and 95% confidence intervals were calculated. CONCLUSIONS: The treatment of VDUFs with nonoperative management, ORIF, or distal ulna resection may all be acceptable treatment options in specific patient populations. Nonoperative management of VDUFs is a promising treatment strategy even for complex fracture patterns in patients 65 years of age or older. Despite higher reoperation rates, ORIF may be considered for the younger, high-demand patient. Distal ulna resection presents with very favorable functional outcomes in patients 65 years of age or older presenting with a complex VDUF with the lowest reoperation rate across all groups. LEVEL OF EVIDENCE: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".