Static and Dynamic External Fixation are Equally Effective for Unstable Elbow Fracture-Dislocations
Bibliographic record
Abstract
OBJECTIVE: To compare the clinical outcomes of static versus dynamic external fixation for elbow fracture-dislocations with persistent instability after surgical management. DESIGN: Comparative, retrospective review. SETTING: Two tertiary referral upper-extremity centers. PATIENTS: Twenty-four elbows requiring external fixation for persistent elbow instability within 90 days of surgical management of an elbow fracture-dislocation. INTERVENTION: Static and dynamic external fixation was used in 16 and 8 patients, respectively, for a median of 39 days (interquartile range, 33-48 days). MAIN OUTCOME MEASUREMENTS: Elbow range of motion, complications, and revision surgeries. RESULTS: Immediately after static and dynamic external fixation removal, there was no difference in elbow extension [33 degrees ± 16 degrees vs. 41 degrees ± 13 degrees, mean difference (MD) 7 degrees, 95% confidence interval (CI) -6 degrees-22 degrees] or flexion (114 degrees ± 35 degrees vs. 118 degrees ± 11 degrees, MD 4 degrees, 95% CI -23 degrees-132 degrees), respectively. At last follow-up, static and dynamic external fixation groups had no difference in elbow extension (27 degrees ± 13 degrees vs. 24 degrees ± 10 degrees, MD -3 degrees, 95% CI -15 degrees-7 degrees) or flexion (129 degrees ± 12 degrees vs. 128 degrees ± 14 degrees, MD -1 degree, 95% CI -13 degrees-10 degrees), respectively. Static and dynamic external fixation groups had no difference in complications [7 (44%) vs. 5 (63%), difference 19%, 95% CI -23%-54%] or revision surgeries [6 (38%) vs. 4 (50%), difference 13%, 95% CI -27%-49%]. CONCLUSIONS: No difference in range of motion, complications, and revision surgeries was detected after static versus dynamic external fixation of persistently unstable elbow fracture-dislocations. Due to ease of application, static external fixation is our preferred treatment for these injuries. 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".