Abstract
A systematic review begins with someone reading thousands of abstracts to identify the few that are relevant, and classifiers are used to prioritise that reading. Their inputs are often augmented with Medical Subject Headings (MeSH), assigned either by expert indexers weeks or months after publication or by automatic tools at once. We did not identify prior work comparing the two directly as classifier features, or asking whether that comparison's outcome depends on how the classifier is evaluated. Using the Cohen et al. (2006) drug-class benchmark, we compare expert assignment against one mechanical procedure, substring matching against a MeSH vocabulary drawn from the benchmark, across a bag-of-words logistic regression classifier (seven reruns) and BiomedBERT (five seeds) on three topics. Under the canonical 5-fold full-corpus design the bag-of-words gap on Statins is +0.096 WSS@95%. Stratified subsampling to matched corpus size (n=803) reduces it by roughly two thirds, to +0.033, with a bootstrap interval that includes zero; 10-fold cross-validation at full corpus size reduces it by roughly four fifths, to +0.021. BiomedBERT under canonical evaluation gives +0.020, a difference of 0.001 from the bag-of-words 10-fold result. An empirical power analysis on a single canonical run per topic indicates that a Statins-sized effect at the per-fold variances of the other two topics would not have been detectable at that design (MDE 0.254 for Opioids, 0.384 for ADHD); at the pooled fold count of the multi-run protocol the bound depends on an effective sample size the design does not determine. The results bound the specific lexical matcher tested rather than automatic MeSH indexing in general. More broadly, benchmark conclusions about feature sources can change substantially under reasonable changes to the evaluation design.
Explore similar work
Date pendingcs.CL
Matching submissions with suitable reviewers at scale is a growing challenge for major venues, yet existing approaches either rely on coarse proxy signals that conflate general relatedness with true suitability, or require expensive human annotations that are difficult to scale for training. We propose MERIT, a two-stage framework that bridges this gap by converting criterion-level expertise matching into scalable suitability supervision. In the first stage, we train a reviewer assessor via reinforcement learning to identify the expertise dimensions a paper requires, match them against the reviewer's prior work, and produce a suitability decision, with rewards provided by an LLM judge guided by paper-specific expertise rubrics. In the second stage, we distill the assessor's predictions into an embedding-based retriever for efficient large-scale assignment. Experiments show that our 4B reviewer assessor outperforms larger general-purpose LLMs on suitability classification, and the resulting retriever achieves state-of-the-art performance across LR-Bench and the CMU Gold dataset. Our code is available at https://github.com/Luli3220/MERIT.
Zixuan Yang, Yibo Zhao, Weicong Liu +1
Apr 17, 2026cs.IR
Effective biomedical information retrieval requires modeling domain semantics and hierarchical relationships among biomedical texts. Existing biomedical generative retrievers build on coarse binary relevance signals, limiting their ability to capture semantic overlap. We propose BioHiCL (Biomedical Retrieval with Hierarchical Multi-Label Contrastive Learning), which leverages hierarchical MeSH annotations to provide structured supervision for multi-label contrastive learning. Our models, BioHiCL-Base (0.1B) and BioHiCL-Large (0.3B), achieve promising performance on biomedical retrieval, sentence similarity, and question answering tasks, while remaining computationally efficient for deployment.
Mengfei Lan, Lecheng Zheng, Halil Kilicoglu
Jun 15, 2026cs.CL
Meta-analysis is a demanding form of evidence synthesis that combines literature retrieval, PI/ECO-guided study selection, and statistical aggregation. Its structured, verifiable workflow makes it an ideal substrate for evaluating systematic scientific reasoning, yet existing benchmarks lack ground truth across the full retrieval-screening-synthesis pipeline. We introduce MetaSyn, a dataset of 442 expert-curated meta-analyses from Nature Portfolio journals. Each entry pairs a research question with PI/ECO criteria, a retrieval corpus of 140k PubMed articles, verified positive studies, hard negatives that are topically similar but PI/ECO-ineligible, and complete search strategies and date bounds. Benchmarking twelve pipeline configurations (nine RAG variants and a protocol-driven agent) reveals a critical screening bottleneck: despite a retrieval ceiling of 90.9% recall at K=200, no system recovers more than 52.7% of ground-truth included literature. Current LLMs fail to reliably separate eligible studies from PI/ECO-failing distractors in pools of comparable topical relevance. Stage-attributed metrics capture where systems succeed and fail; a single end-to-end score does not.
Anzhe Xie, Weihang Su, Yujia Zhou +2