Endometrial cancer (EC) is the most common gynecologic malignancy in developed countries, with rising incidence due to aging populations and obesity-related factors. This review explores the evolving molecular and FIGO classifications of EC, highlighting their significance in diagnosis, prognosis, and personalized treatment strategies. Molecular subtyping based on The Cancer Genome Atlas (TCGA) classification offers a more precise understanding of EC, dividing it into POLE ultramutated, microsatellite instability-high (MSI-H), copy-number low (CNL), and copy-number high (CNH) subtypes. Each subgroup has distinct genetic, histological, and prognostic characteristics. Recent updates to the FIGO staging system incorporate molecular features, allowing for more tailored treatment approaches. Advances in immunotherapy, targeted therapies, and novel therapeutic combinations have reshaped clinical management. This review emphasizes the integration of molecular diagnostics into routine practice, outlining challenges and future perspectives in managing EC for improved patient outcomes.

Background and Objective: To correlate the intraocular pressure with the postconceptional age and identify a statistically significant connection between congenital glaucoma and prematurity. Materials and Methods: The current paper is a retrospective, comparative, case-control study. Data collection featured maternal age, gestational age at birth, birth weight, and intraocular ocular pressure (IOP) measurements. Results: Forty-two eyes of 21 children underwent examination. The participants were assigned into two groups. The Preterm-Glaucoma (PG) group included eight preterm-born children diagnosed with glaucoma, whereas the Preterm (P) group was comprised of premature newborns without the aforementioned condition. There was no statistically relevant difference in birth weight (p = 0.691078) nor in mean gestational age (p = 0.752623) between the two groups. The mean IOP in the PG group was 23.813 ± 4.5493, whereas in the P group, it ranged around 13.231 ± 1.0699, p < 0.0001. Using mixed-effects models, we obtained a reduction in IOP of 0.45 mmHg per week in the first month of life. A further weekly reduction of 0.36 mmHg was achieved in the next two months. Conclusions: The mean IOP of prematurely born children decreased with age. Our findings correlate with previously conducted studies, however, the drop in IOP values exceeded any data published so far. We found no correlation between prematurity and the incidence of congenital glaucoma.

Background and objectives: Large language models (LLMs) show promise in automating open-ended evaluation tasks, yet their reliability in rubric-based assessment remains uncertain. Variability in scoring, feedback, and rubric adherence raises concerns about transparency and pedagogical validity in educational contexts. This study introduces CourseEvalAI, a framework designed to enhance consistency and fidelity in rubric-guided evaluation by fine-tuning a general-purpose LLM with authentic university-level instructional content. Methods: The framework employs supervised fine-tuning with Low-Rank Adaptation (LoRA) on rubric-annotated answers and explanations drawn from undergraduate computer science exams. Responses generated by both the base and fine-tuned models were independently evaluated by two human raters and two LLM judges, applying dual-layer rubrics for answers (technical or argumentative) and explanations. Inter-rater reliability was reported as intraclass correlation coefficient (ICC(2,1)), Krippendorff’s α, and quadratic-weighted Cohen’s κ (QWK), and statistical analyses included Welch’s t tests with Holm–Bonferroni correction, Hedges’ g with bootstrap confidence intervals, and Levene’s tests. All responses, scores, feedback, and metadata were stored in a Neo4j graph database for structured exploration. Results: The fine-tuned model consistently outperformed the base version across all rubric dimensions, achieving higher scores for both answers and explanations. After multiple-testing correction, only the Generative Pre-trained Transformer (GPT-4)—judged Technical Answer contrast remains statistically significant; other contrasts show positive trends without passing the adjusted threshold, and no additional significance is claimed for explanation-level results. Variance in scoring decreased, inter-model agreement increased, and evaluator feedback for fine-tuned outputs contained fewer vague or critical remarks, indicating stronger rubric alignment and greater pedagogical coherence. Inter-rater reliability analyses indicated moderate human–human agreement and weaker alignment of LLM judges to the human mean. Originality: CourseEvalAI integrates rubric-guided fine-tuning, dual-layer evaluation, and graph-based storage into a unified framework. This combination provides a replicable and interpretable methodology that enhances the consistency, transparency, and pedagogical value of LLM-based evaluators in higher education and beyond.

Large Language Models (LLMs) are increasingly used for rubric-based assessment, but reliable automated grading requires more than a single prompt-response step. This study presents GradeAgentOps, a verification-first framework for evidence-anchored LLM exam grading that combines strict grading contracts, deterministic verification and canonicalization, bounded semantic repair, optional memory modules, and provenance-aware logging. The framework is evaluated on a university-level dataset of 1000 short open-ended exam responses from 100 students across 10 questions, with annotations from two independent expert human graders. A controlled ablation protocol compares six configurations, including a rubric-only baseline and progressively stronger variants with repair and memory augmentation. Human–human agreement provides the reference context, with overall ICC(2,1) = 0.678 and QWK = 0.678 between the two graders. Using one expert grader as the operational reference, the FULL configuration achieves the strongest model-human agreement (MAE = 1.935, RMSE = 2.500, QWK = 0.652, Within ±2 = 0.667), with the consistency memory configuration with repair (C1) emerging as the closest alternative. The gains are not uniform: improvements are more pronounced on argumentative items than on technical ones, and pairwise comparisons show that consistency memory contributes more strongly than rubric memory alone. The stronger configurations also produce cleaner accepted outputs, with lower rates of evidence-related postprocess issues, although at a moderate operational cost. Overall, within this controlled single-course evaluation setting, the results show that reliable automated grading benefits not only from model capability, but also from pipeline design choices that promote verification, evidential coherence, and stable grading behavior.

Large Language Models (LLMs) are increasingly used for rubric-based assessment, yet reliability is limited by instability, bias, and weak diagnostics. We present EvalCouncil, a committee-and-chief framework for rubric-guided grading with auditable traces and a human adjudication baseline. Our objectives are to (i) characterize domain structure in Human–LLM alignment, (ii) assess robustness to concordance tolerance and panel composition, and (iii) derive a domain-adaptive audit policy grounded in dispersion and chief–panel differences. Authentic student responses from two domains–Computer Networks (CNs) and Machine Learning (ML)–are graded by multiple heterogeneous LLM evaluators using identical rubric prompts. A designated chief arbitrator operates within a tolerance band and issues the final grade. We quantify within-panel dispersion via MPAD (mean pairwise absolute deviation), measure chief–panel concordance (e.g., absolute error and bias), and compute Human–LLM deviation. Robustness is examined by sweeping the tolerance and performing leave-one-out perturbations of panel composition. All outputs and reasoning traces are stored in a graph database for full provenance. Human–LLM alignment exhibits systematic domain dependence: ML shows tighter central tendency and shorter upper tails, whereas CN displays broader dispersion with heavier upper tails and larger extreme spreads. Disagreement increases with item difficulty as captured by MPAD, concentrating misalignment on a relatively small subset of items. These patterns are stable to tolerance variation and single-grader removals. The signals support a practical triage policy: accept low-dispersion, small-gap items; apply a brief check to borderline cases; and adjudicate high-dispersion or large-gap items with targeted rubric clarification. EvalCouncil instantiates a committee-and-chief, rubric-guided grading workflow with committee arbitration, a human adjudication baseline, and graph-based auditability in a real classroom deployment. By linking domain-aware dispersion (MPAD), a policy tolerance dial, and chief–panel discrepancy, the study shows how these elements can be combined into a replicable, auditable, and capacity-aware approach for organizing LLM-assisted grading and identifying instability and systematic misalignment, while maintaining pedagogical interpretability.