Background: Midfacial fractures are common outcomes of facial trauma. While younger individuals typically sustain these injuries through high-energy events like assaults and traffic or sports accidents, elderly patients increasingly present with fractures from low-energy mechanisms, primarily falls. Purpose: The aim of this study was to analyze age- and gender-specific patterns in midfacial fractures over a 10-year period, with emphasis on elderly individuals and low-energy trauma. Methods: A retrospective review was performed of proven midfacial fractures between 2013 and 2022 at the Department of Oral and Maxillofacial Surgery (University of Pécs, Hungary). The patients were stratified by age (<65 vs. ≥65 years) and gender. The variables included the injury mechanism, fracture localization, the dental status, hospitalization, and the presence of associated injuries. Bivariate analyses were performed, and the significance level was set to p < 0.05. Results: A total of 957 radiologically confirmed midfacial fracture cases were evaluated, of whom 344 (35.9%) were ≥65 years old. In the elderly group, females had a 19-fold higher risk for midfacial trauma than younger females (OR: 19.1, 95%CI: 9.30–39.21). In the older group, a fall was significantly the most frequent injury mechanism (OR: 14.5; 95%CI: 9.9–21.3), responsible for 89.5% of the cases, while hospitalization (OR: 0.36; 95%CI: 0.23–0.56) was less characteristic. Most of the fractures occurred in the zygomatic bone, in the zygomaticomaxillary complex, or in the anterior wall of the maxilla. Associated injuries in the elderly group included mostly lower limb injuries—particularly pertrochanteric femoral fractures in females—and upper limb injuries, with a slight male dominance. Conclusions: Low-energy falls are the primary cause of midfacial fractures in elderly patients, particularly in women. Tailored prevention and management strategies are essential for improving the outcomes in this growing demographic group.

As multimodal large language models (LLMs) are applied to computed tomography (CT) and magnetic resonance imaging (MRI), how agreement between an artificial intelligence (AI) system and the radiologist is measured determines whether reported progress is believable. This structured narrative review maps the metric families for AI–radiologist concordance on CT/MRI reports and assesses each against a demanding setting: whole-study analysis of real-world data, where an individual submits a single study without priors, laboratory data or indication, and the radiologist report is an imperfect, non-exhaustive reference, a premise anchored in the radiologist discrepancy literature. We reviewed lexical, clinical/factuality, composite and LLM-as-judge metrics; inter-rater agreement statistics (the kappa family, Gwet’s AC1, PABAK, Krippendorff’s alpha, intraclass correlation) and the unit of agreement each presupposes; LLM-as-judge biases; and reporting standards. Entity-based and LLM-as-judge metrics with explicit error taxonomies align best with radiologist judgement but require validation. A worked example shows Cohen’s and Fleiss’ kappa collapsing under the class imbalance typical of findings data; prevalence-robust coefficients are more trustworthy, and Fleiss’ kappa belongs to multi-reader panel reliability, not AI-versus-report concordance. We recommend a layered metric stack anchored on significance-tiered finding recall, computed by an LLM judge validated as non-inferior to human–human agreement, and reported under CLAIM 2024, STARD-AI and TRIPOD-LLM.