Anteromedial facet coronoid fractures: an algorithmic approach for assessment and management based on 3 dimensional computed tomography humeral subtraction
Bibliographic record
Abstract
Background: Isolated coronoid fractures without concomitant radial head injury raise suspicion for an anteromedial facet (AMF) fracture as a result of a varus posteromedial rotatory instability (VPMRI) mechanism. However, not all isolated coronoid fractures involve the AMF, nor are they all the result of a VPMRI mechanism. AMF fractures as a result of a VPMRI mechanism have been reported to have specific radiographic features (medially oblique, concave, with extension to the sublime tubercle). We hypothesized that a detailed assessment of the pattern of AMF fracture utilizing three-dimensional computed tomography with digital subtraction of the humerus allows for a reliable treatment algorithm to avoid over- or under-treatment of these complex injuries. Methods: This was a retrospective case series of patients with isolated coronoid fractures involving the AMF. Patients with an AMF fracture meeting radiographic features consistent with a VPMRI mechanism were managed operatively, and those with an AMF fracture that did not meet one of the radiographic features of a VPMRI mechanism were considered for nonoperative management. Our primary outcome was the appropriateness of this treatment algorithm by assessing whether any patients in the nonoperative group required later surgical intervention or developed any evidence of subluxation or degenerative changes. Secondary outcomes were elbow and forearm range of motion, functional outcome, patient-reported outcomes, and complications at the final follow-up. Results: 43 patients with a minimum of 6 months of follow up were reviewed. 28 patients met all radiographic features of VPMRI and underwent surgical stabilization. After an average follow up of 17 ± 14 months, they achieved an elbow arc of 130° ± 19°, forearm arc of 153° ± 13°, Mayo Elbow Performance Index of 98.4 ± 4.0, quick disabilities of the arm, shoulder, and hand of 8.3 ± 9.1, patient-rated elbow evaluation of 18.2 ± 9.1, and a single assessment numeric evaluation score of 90% ± 11%. The incidence of overall complications was 64%. 15 remaining patients did not meet at least one radiographic feature of VPMRI and all but one were managed nonoperatively. After an average follow up of 15 ± 13 months, they achieved an elbow arc of 136° ± 8°, forearm arc of 158° ± 12°, Mayo Elbow Performance Index of 99.0 ± 3.7, quick disabilities of the arm, shoulder, and hand of 6.8 ± 4.8, patient-rated elbow evaluation of 15.9 ± 6.3, and a single assessment numeric evaluation score of 93% ± 7%. Importantly, no patient developed any evidence of late instability, incongruity, or degenerative changes. Conclusion: The proposed algorithm based on a detailed assessment of the pattern of AMF fracture provides a reliable decision tool for the management of these injuries. Utilizing this decision tool and appropriate nonoperative or operative intervention can lead to good-to-excellent clinical and radiographic outcomes. Larger cohorts are required to confirm these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".