Varus Posteromedial Rotatory Instability of the Elbow: Injury Pattern and Surgical Experience of 27 Acute Consecutive Surgical Patients
Bibliographic record
Abstract
OBJECTIVES: To identify associated injuries that occur in varus posteromedial rotatory instability (VPMRI) of the elbow and present their surgical management. DESIGN: Level II retrospective study. SETTING: Tertiary referral center. PATIENT/PARTICIPANTS: Twenty-seven patients with VPMRI injuries treated surgically over an 8-year period. INTERVENTION: Open reduction and internal fixation of anteromedial coronoid facet fracture, lateral collateral ligament repair, and associated injured soft-tissue repairs. MAIN OUTCOME MEASURED: Radiographic classification, associated medial and lateral bony and soft-tissue injuries, surgical fixation method, and complications were recorded. RESULTS: According to the O'Driscoll classification, there were 15 (55%) type 2-2, 11 (41%) type 2-3, and 1 (4%) type 3-1 fractures. Lateral and medial collateral ligament tears were found in 100% and 63%, respectively. Common extensor and flexor origin injuries occurred in 19 (70%) and 2 (7%) elbows, respectively. A marginal radial head fracture was found in 1 patient. Most patients were treated with a combination of fixation methods. Complications occurred in 7 (26%) patients. CONCLUSIONS: This study documents both associated findings and surgical fixation methods. In all cases, the lateral collateral ligament was disrupted, often in association with an injured common extensor origin. Medial collateral ligament injuries are commonly involved. Radial head fractures are rarely associated. The surgeon should have a high index of suspicion if an isolated coronoid fracture is encountered. Clinical and functional outcome scores are needed in future studies to further inform treatment of VPMRI of the elbow. 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".