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C. Andrei

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Review Open access Sep 2026

Key Inflammatory Pathways, Biomarkers, and Targeted Management Strategies in Primary Total Joint Arthroplasty: A Narrative Review

Total joint arthroplasty is a surgical procedure with rising global incidence. Although a strong postoperative inflammatory response is necessary for tissue repair following primary arthroplasty, it may prove to be harmful if excessive or prolonged. This could compromise osseointegration, increase pain, and delay the d...

Adelina-Elena Moise, M. Gherghe, A. Grigore et al. · 0 citations
Open access Aug 2026

CreditTrace-LLM: Auditing Rubric-Point Responsiveness and Credit Locality in LLM-Based Automated Grading

CreditTrace-LLM evaluates target responsiveness, credit locality, paraphrase stability, and traceable Gold-point-level outputs as complementary diagnostic dimensions rather than as predefined criteria for acceptable grading performance.

Cătălin Anghel, A. Anghel, M. Craciun et al. · 0 citations
Open access Aug 2026

From Medical Records to AI-Ready Datasets: A Practical Guide for Clinical Researchers

A physician-facing Clinical AI-Readiness Guide for preparing medical datasets before AI-based analysis to improve collaboration between clinical and technical teams and reduce preventable dataset-related failures in medical AI research is proposed.

Cătălin Anghel, A. Anghel, M. Craciun et al. · 0 citations
Open access Aug 2026

ConsensusGrade: A Human-Variability-Aware Framework for Evaluating LLM-Based Automated Grading

ConsensusGrade provides a diagnostic framework for interpreting automated scores relative to observed human variability; inside-envelope rates should not be interpreted as stand-alone measures of grading accuracy.

Cătălin Anghel, A. Anghel, Mihai Vlase et al. · 0 citations
Open access Aug 2026

GradeDrift-LLM: Measuring Student-History-Induced Score Drift in LLM-Based Automated Grading

Student-history metadata can influence LLM-generated grading scores despite explicit instructions to ignore it, and future LLM-based grading systems should separate answer-based scoring from learner-context-based personalization and validate score invariance under controlled learner-context variations.

Cătălin Anghel, A. Anghel, M. Craciun et al. · 0 citations

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