Competitive balance in the big five European leagues based on Shannon entropy

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗

bioRxiv2018

Violating the normality assumption may be the lesser of two evils

When data are not normally distributed (e.g. skewed, zero-inflated, binomial, or count data) researchers are often uncertain whether it may be legitimate to use tests that assume Gaussian errors (e.g. regression, t-test, ANOVA, Gaussian mixed models), or whether one has to either model a more specific error structure or use randomization techniques.Here we use Monte Carlo simulations to explore the pros and cons of fitting Gaussian models to non-normal data in terms of risk of type I error, power and utility for parameter estimation.We find that Gaussian models are remarkably robust to non-normality over a wide range of conditions, meaning that P-values remain fairly reliable except for data with influential outliers judged at strict alpha levels. Gaussian models also perform well in terms of power and they can be useful for parameter estimation but usually not for extrapolation. Transformation of data before analysis is often advisable and visual inspection for outliers and heteroscedasticity is important for assessment. In strong contrast, some non-Gaussian models and randomization techniques bear a range of risks that are often insufficiently known. High rates of false-positive conclusions can arise for instance when overdispersion in count data is not controlled appropriately or when randomization procedures ignore existing non-independencies in the data.Overall, we argue that violating the normality assumption bears risks that are limited and manageable, while several more sophisticated approaches are relatively error prone and difficult to check during peer review. Hence, as long as scientists and reviewers are not fully aware of the risks, science might benefit from preferentially trusting Gaussian mixed models in which random effects account for non-independencies in the data in a transparent way.Tweetable abstract Gaussian models are remarkably robust to even dramatic violations of the normality assumption.Competing Interest StatementThe authors have declared no competing interest.View Full Text

scientific communication and education↗

bioRxiv2018

Intentions to use patient-initiated partner notification and acceptability of provider-initiated partner notification for Sexually Transmitted Infections - A cross-sectional survey among minibus taxi drivers in Gauteng Province, South Africa

BackgroundIn South Africa, utilization of patient-initiated partner-notification (PN) using referral-slip in the management of sexually transmitted infections (STIs) is limited and only a limited number of sexual partners are ever notified. The study assessed the use of patient-initiated PN method using notification and referral slips and measured the level of acceptability of provider-initiated PN using short-message-service (SMS) to personal mobile phones of sexual partners.\n\nMethodsA quantitative survey using anonymous structured self-administered and researcher assisted questionnaires was conducted among minibus taxi drivers in the nine major taxi ranks in Gauteng province, South Africa.\n\nResultsThe sample consisted of 722 minibus taxi drivers with a mean age of 37.2 years old, 284 (59.5%) had multiple sexual partners, 368 (52.2%) did not use a condom during last sexual act, 286 (42.8%) reported inconsistent use of condoms, and 459 (65%) tested for HIV in the past 12 months. Majority (n=709, 98.2%) understood the importance of PN once diagnosed with STI, but would prefer delivering PN referral slip (n=670, 93.2%) over telling a partner face to face if they themselves were diagnosed with STI. Acceptability of provider-initiated PN using SMS was 452 (62.7%) and associated with history of HIV testing in the past year (OR=1.72, p=0.002, CI: 1.21-2.45). The perceived use of PN referral-slip from sexual partner to seek treatment was 91.8% (n=659). About a third (n=234, 32.5%) were not in favor of provider-initiated PN by SMS and preferred telling partners face to face.\n\nConclusionThere were contrasting findings on the acceptability and utilization of existing patient-initiated PN and the proposed PN using SMS from health providers. The preference of delivering PN referral slip to sexual partner over face-to-face PN renders communicating about STIs the responsibility of health providers. Therefore, they have an opportunity to provide patients with options to choose a PN method that is best suited to their relationships and circumstances and modify PN messages to encourage partners to use the different PN to prevent STIs.

scientific communication and education↗