(1) Background: Proximal humerus fractures can be a debilitating condition if not properly treated. These fracture patterns are varied and differ in every patient. Functional outcomes may be determined by the integrity of the shoulder girdle involving the rotator cuff insertion. The post-operative resorption or malreduction of the greater tuberosity is an important factor contributing to the poor functional outcome of a patient. (2) Methods: A single-center retrospective comparative study was performed to evaluate the functional outcomes of patients undergoing operative fixation for this injury. A total of 387 consecutive cases treated operatively from 2019-2021 were included for analysis. (3) Results: 94 cases fulfilled our criteria for analysis. A matched-group comparison of 19 patients each was performed to compare demographics, post-operative fracture characteristics and functional outcomes. (4) Conclusions: The resorption or malreduction of the greater tuberosity contributes greatly to the prognostic outcome in patients treated with ORIF surgery. In our demographic study, obesity is another contributing factor affecting the parameters of post-operative reduction in proximal humerus fractures. Appropriate surgical planning and post-operative multidisciplinary care must be taken into consideration to attain a satisfactory prognostic outcome.

Combining Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility test (AST) of S. aureus. Based on the AI predictive probability, the cases with probabilities between low and high cut-offs are defined as “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. A total 479 S. aureus isolates were collected, analyzed by MALDI-TOF, and AST prediction, standard AST were obtained in a tertiary medical center. The predictions were categorized into the correct prediction group, wrong prediction group, and grey zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For MRSA, larger cefoxitin zone size was found in the wrong prediction group. MLST of the MRSA isolates in the grey zone group revealed that uncommon strain types composed 80%. Amid MSSA isolates in the grey zone group, the majority (60%) was composed of over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity would contribute to suboptimal predictive performance.