Medical question answering (QA) systems require both factual reliability and computational efficiency for real-world deployment. We address this dual challenge by introducing a resource-constrained framework that integrates a fine-tuned 1.5B-parameter Small Language Model with retrieval-augmented generation (RAG) and a calibrated fact-checking module specifically optimized for medical domain constraints. Our key contribution lies not merely in component integration, but in demonstrating how internal system metrics, particularly our Factual Consistency Score (FCS) serve as effective Query Performance Prediction (QPP) indicators for answer reliability in complex RAG pipelines, a critical gap in current IR paradigms. Evaluated on PubMedQA and standard medical benchmarks, our approach achieves competitive performance while operating on a single T4 GPU with a 7 GB memory footprint, requiring 85% fewer resources than comparable 7B-parameter medical LLMs. The framework reduces hallucinations by 37% (measured by FCS) compared to non-fact-checked baselines, though it currently has limitations in handling multi-hop medical reasoning and cross-sentence verification. Our work provides a practical blueprint for developing accessible, trustworthy medical QA systems that balance performance with infrastructure constraints, establishing that internal consistency metrics can effectively predict answer quality where traditional QPP methods fall short. The implementation demonstrates that resource-constrained environments need not sacrifice reliability when thoughtfully designed verification mechanisms are integrated into the retrieval-generation pipeline.

Batsmen are accorded a certain precedence for better batting ability over their peers. The batsmen in cricket are assessed mostly based on their batting average. However, comparing players by batting average over different timelines does not yield the appropriate results, as the batting productivity of a particular player varies in unique ways across the different formats of the game. Using batting averages for comparison does not include factors such as the speed of scoring runs and the frequency of milestones achieved. The objective of this study is to present an effective knowledge-based mechanism for judging and comparing the batting strength of players in different formats of cricket. This methodology uses a complex network consisting of effective features that are subsequently integrated to formulate a Batting Precedence Score, which is further incorporated into an efficient Batting Precedence Score algorithm. In addition, we created a structured World Wide Batsman Dataset (WWBD) for our analysis based on the ESPN Cricinfo dataset. The results of extensive experiments demonstrate that the proposed method provides promising insights. The batting precedence of the proposed method is further compared with those of existing methods, and the proposed method displays better results.