Multi-agent large language model (LLM) systems increasingly combine model-generated judgments for forecasting and collective decision-making. However, homogeneous agent systems, i.e., those that share a model and evidence, may produce correlated forecasts, and communication may further contract the diversity that remains. We examine these issues in live LLM prediction polls. Across 189 resolved snapshots, mean residual-error correlation was 0.910. Semantic prompt differences were associated with forecast differences, but diversity-aware selection did not detectably outperform prespecified random ensembles. Standard deliberation removed approximately twice as much disagreement benefit as it added through improvements in individual forecasts at the point estimates, with no detectable improvement in aggregate accuracy. In a prospective intervention, a probability-blind summary of forecast-relevant claims retained 93% of initial dispersion, compared with 29% under standard exchange.

The extraction of structured clinical information from unstructured EHR notes is a persistent bottleneck in healthcare informatics. While large language models (LLMs) offer high performance, their deployment in clinical settings is hindered by privacy risks, inference costs, and the tendency to hallucinate beyond textual evidence. We address these challenges for the CL4Health 2026 Case Report Form (CRF) filling task by proposing a fully local, domain-adapted pipeline using the MedGemma-27B model. Our two-stage architecture, which separates binary presence classification from value extraction, enforces strict adherence to textual evidence and ensures deterministic outputs for negated, uncertain, or unknown states. By leveraging item-specific, few-shot in-context learning without external API calls or fine-tuning, our approach achieves a macro-F1 score of 0.55 on the official English test track. This result secures second place among all locally-hosted, open-source submissions. Our work demonstrates that privacy-preserving, on-premise LLM pipelines can achieve near-competitive performance with proprietary frontier models, providing a practical, data-sovereign framework for clinical NLP.