A Fragment-Specific Approach to Type IID Monteggia Elbow Fracture–Dislocations
Bibliographic record
Abstract
OBJECTIVES: To describe the pattern of injury, surgical technique, and outcomes of Monteggia Type IID fracture dislocations. DESIGN: Retrospective review of prospectively collected clinical and radiographic patient data in an orthopaedic trauma database. SETTING: Level I university-based trauma center. PATIENTS/PARTICIPANTS: All patients with Monteggia Type IID fracture-dislocations admitted from January 2000 to July 2005. INTERVENTION: Review of patient demographics, fracture pattern, method of fixation, complications, additional surgical procedures, and clinical and radiographic outcome measures. MAIN OUTCOME MEASUREMENTS: Clinical outcomes: elbow range of motion, complications. Radiographic outcomes: characteristic fracture fragments, quality of fracture reduction, healing time, degenerative changes, and heterotopic ossification. RESULTS: Sixteen patients were included in the study. All fractures united. There were six complications in six patients, including three contractures with associated heterotopic ossification, one pronator syndrome and late radial nerve palsy, one radial head collapse, and one with prominent hardware. CONCLUSIONS: Monteggia IID fracture-dislocations are complex injuries with typical specific fracture fragments. Anatomic fixation of all injury components and avoidance of complications where possible can lead to a good outcome in these challenging injuries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".