Research Domain Criteria (RDoC) is a National Institute of Mental Health framework for studying mental disorders by integrating information across genetics, circuits, and behavior. Manually curating biomedical abstracts relevant to RDoC is a significant challenge due to semantically overlapping construct definitions (e.g., "Acute Threat," "Potential Threat," and "Sustained Threat") and the exponential growth of biomedical literature. This study compares two modeling strategies, domain-adapted fine-tuning and in-context prompting, across two RDoC-related subtasks from the official BioNLP-OST 2019 RDoC shared task. For Task 1, unlabeled PubMed abstracts are retrieved and ranked by relevance to eight of the RDoC constructs. We compare a TF-IDF baseline against ModernBERT and Llama (zero-shot and five-shot) using Mean Average Precision (MAP). For Task 2, the objective is to identify the single most relevant sentence from an abstract for a given construct, evaluated using per-construct accuracy. The fine-tuning track performs end-to-end fine-tuning of BioBERT, PubMedBERT, ModernBERT, and RoBERTa using a cross-encoder input format and per-construct grid search. These are compared against the in-context learning of several open-source language models. Both our approaches are competitive against the best-performing team’s score from the BioNLP-OST 2019 RDoC shared task. Taken together, these findings suggest that five-shot prompted LLMs and domain-adapted fine-tuned transformers are viable tools for semi-automating the expert annotation in RDoC curation.