Accuracy of PSI-based hybrid workflow using a temporary intermediate splint versus conventional splint-based maxillary positioning in orthognathic surgery: a retrospective cohort study
This retrospective cohort study, adhering to STROBE guidelines, compared the accuracy of maxillary positioning achieved with a patient-specific implant (PSI)-based hybrid workflow, which utilised a temporary intermediate splint to identify and remove posterior bony interferences before definitive splintless PSI fixation, to conventional splint-based positioning using stock titanium osteosynthesis plates in orthognathic surgery. Translational and rotational movement deviations from the virtual surgical plan were assessed. Twenty-six patients who underwent non-segmental Le Fort I osteotomy between January 2020 and June 2023 were included. Eleven patients received the PSI-based hybrid workflow, while 15 patients underwent conventional splint-based positioning. Group allocation was based on the logistical feasibility of PSI planning and manufacturing rather than randomisation. Pre- and postoperative 3D images were superimposed using IPS® Case Designer software (KLS Martin) to quantify deviations in three translational and three rotational axes. Between-group comparisons were conducted using unpaired t-tests and two-way ANOVA, and correlations were assessed using Pearson’s coefficient. The mean translational deviation was significantly lower in the PSI group compared to the control group (0.46 ± 0.33 mm vs 0.79 ± 0.58 mm; p = .0044), as was the mean rotational deviation (0.82° ± 0.91° vs 1.24° ± 0.85°; p = .0397). Significant differences were identified for vertical translation (p = .0162) and yaw rotation (p = .0205). Operating time did not differ significantly between groups (p = . 1739). In the PSI group, greater planned displacement showed a weak correlation with larger deviations, a relationship not observed in the control group. The PSI-based hybrid workflow resulted in statistically greater positioning accuracy compared to conventional splint-based fixation. However, the absolute improvements (approximately 0.3 mm and 0.5°) were small, likely of limited clinical relevance, and were not associated with reduced operating times. Due to the retrospective, non-randomised design, small sample size, and use of a PSI-based hybrid protocol, these findings should be considered hypothesis-generating. Larger prospective studies evaluating fully splintless PSI workflows and patient-centred outcomes are recommended. Not applicable.
MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.
James C. Davis, Kelechi G. Kalu, Huiyun Peng et al.· 1 citation
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
Albérick Euraste Djiré, Iyiola E. Olatunji, Melissa Tessa et al.· 1 citation
HawkEye is introduced, a modular, web-based vulnerability auditing platform designed to streamline security analysis by integrating multiple scanning tools within a unified dashboard and illustrates how consolidated reporting improves vulnerability prioritization for development teams.
D. R. Patil, Varad Salgare, Devaj Arya et al.· International Journal for Re...· 0 citations
By streamlining workflows and fostering collaboration, this platform offers a scalable, cost- effective solution for SMEs and contributes to software engineering by demonstrating how integrated technologies can modernize development processes in resource limited contexts, with potential for broader adoption in Albania and beyond.
The findings of the present study indicated that potential complications such as delayed union, nonunion, and osteomyelitis in the intramedullary nailing method are approximately comparable to those of the external fixator method.
Reza Noktesanj, Ali Nami, F. Amani et al.· journal of Health Research a...· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduAug 17, 2026
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.