Moving beyond word frequency based on tally counting: AI-generated familiarity estimates of words and phrases are an interesting additional index of language knowledge.

This study investigates the potential of large language models (LLMs) to estimate the familiarity of words and multi-word expressions (MWEs). We validated LLM estimates for isolated words using existing human familiarity ratings and found strong correlations. LLM familiarity estimates performed even better in predicting lexical decision and naming performance in megastudies than the best available word frequency measures.

Homozygous deleterious variants in MYCBPAP induce asthenoteratozoospermia involving abnormal acrosome biogenesis, manchette structure and sperm tail assembly in humans and mice.

Asthenoteratozoospermia is a common cause of male infertility. To further define the genetic causes underlying asthenoteratozoospermia, we performed whole-exome sequencing in a cohort of Han Chinese men with asthenoteratozoospermia. Homozygous deleterious variants of MYCBPAP were first identified in two unrelated Chinese cases.

Establishing vocabulary tests as a benchmark for evaluating large language models.

Vocabulary tests, once a cornerstone of language modeling evaluation, have been largely overlooked in the current landscape of Large Language Models (LLMs) like Llama 2, Mistral, and GPT. While most LLM evaluation benchmarks focus on specific tasks or domain-specific knowledge, they often neglect the fundamental linguistic aspects of language understanding. In this paper, we advocate for the revival of vocabulary tests as a valuable tool for assessing LLM performance.

[Lyme's disease. A critical analysis of its presence in Argentina].

Introduction: Lyme disease (LD) is caused by bacteria of the Borrelia burgdorferi sensu lato complex and transmitted by the bite of ticks of the Ixodes ricinus complex. To date, no autochthonous cases have been reported in Argentina. Its diagnosis involves serological tests included in a two-step algorithm.

Examining individual- versus population-level social determinants of health in a cluster randomized trial of health coaches for patients with multiple chronic conditions.

Introduction: Social determinants of health (SDOH) are an important contributor to health status and health outcomes. In this analysis, we compare SDOH measured both at the individual and population levels in patients with high comorbidity who receive primary care at Federally Qualified Health Centers in New York and Chicago and enrolled in the Tipping Points trial.

Methods: We analyzed individual- and population-level measures of SDOH in 1,488 patients with high comorbidity (Charlson Comorbidity Index ≥ 4) enrolled in Tipping Points.