Repetition and reproduction of preclinical medical studies: taking a leaf from the plant sciences with consideration of generalised systematic errors
Reproduction of pre-clinical results has a high failure rate. The fundamental methodology including replication ("protocol") for hypothesis testing/validation to a state allowing inference, varies within medical and plant sciences with little justification. Here, five protocols are distinguished which deal differently with systematic/random errors and vary considerably in result veracity. Aim: to compare prevalence of protocols (defined in text). Medical/plant science articles from 2017/2019 were surveyed: 713 random articles assessed for eligibility for counts: first (with p-values): 1) non-replicated; 2) global; 3) triple-result protocols; second: 4) replication-error protocol; 5) meta-analyses. Inclusion criteria: human/plant/fungal studies with categorical groups. Exclusion criteria: phased clinical trials, pilot studies, cases, reviews, technology, rare subjects, -omic studies. Abbreviated PICOS question: which protocol was evident for a main result with categorically distinct group difference(s) ? Electronic sources: Journal Citation Reports 2017/2019, Google. Triplication prevalence differed dramatically between sciences (both years p<10-16; cluster-adjusted chi-squared tests): From 320 studies (80/science/year): in 2017, 53 (66%, 95% confidence interval (C.I.) 56%:77%) and in 2019, 48 (60%, C.I. 49%:71%) plant studies had triple-result or triplicated global protocols, compared with, in both years, 4 (5%, C.I. 0.19%:9.8%) medical studies. Plant sciences had a higher prevalence of protocols more likely to counter generalised systematic errors (the most likely cause of false positives) and random error than non-replicated protocols, without suffering from serious flaws found with random-Institutes protocols. It is suggested that a triple-result (organised-reproduction) protocol, with Institute consortia, is likely to solve most problems connected with the replicability crisis.
Code (0)
등록된 구현이 없습니다.
Tasks
ArticlesSimilar Papers 제목 키워드 기반
Interpretable Tile-Based Classification of Paclitaxel Exposure
Medical image analysis is central to drug discovery and preclinical evaluation, where scalable, objective readouts can accelerate decision-making. We address classification of paclitaxel (Taxol) exposure from phase-contr…
Drug DiscoveryEvaluating U-net Brain Extraction for Multi-site and Longitudinal Preclinical Stroke Imaging
Rodent stroke models are important for evaluating treatments and understanding the pathophysiology and behavioral changes of brain ischemia, and magnetic resonance imaging (MRI) is a valuable tool for measuring outcome i…
Bringing replication and reproduction together with generalisability in NLP: Three reproduction studies for Target Dependent Sentiment Analysis
Lack of repeatability and generalisability are two significant threats to continuing scientific development in Natural Language Processing. Language models and learning methods are so complex that scientific conference p…
Sentiment AnalysisReinforced Medical Report Generation with X-Linear Attention and Repetition Penalty
To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where attention mechanisms and reinforcement learning are integrated with the cl…
DecoderMedical Report GenerationHybrid Reinforced Medical Report Generation with M-Linear Attention and Repetition Penalty
To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where deep convolutional neural networks (CNNs) are employed to encode the input…
Medical Report Generation