Dataset Information

Going to extremes: the Goldilocks/Lagom principle and data distribution.

ABSTRACT: Numerical data in biology and medicine are commonly presented as mean or median with error or confidence limits, to the exclusion of individual values. Analysis of our own and others' data indicates that this practice risks excluding 'Goldilocks' effects in which a biological variable falls within a range between 'too much' and 'too little' with a region between where its function is 'just right'; a concept captured by the Swedish term 'Lagom'. This was confirmed by a narrative search of the literature using the PubMed database, which revealed numerous relationships of biological and clinical phenomena of the Goldilocks/Lagom form including quantitative and qualitative examples from the health and social sciences. Some possible mechanisms underlying these phenomena are considered. We conclude that retrospective analysis of existing data will most likely reveal a vast number of such distributions to the benefit of medical understanding and clinical care and that a transparent approach of presenting each value within a dataset individually should be adopted to ensure a more complete evaluation of research studies in future.

SUBMITTER: Leese HJ

PROVIDER: S-EPMC6887037 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Going to extremes: the Goldilocks/Lagom principle and data distribution.

Leese Henry J HJ Sathyapalan Thozhukat T Allgar Victoria V Brison Daniel R DR Sturmey Roger R

BMJ open 20191127 11

Numerical data in biology and medicine are commonly presented as mean or median with error or confidence limits, to the exclusion of individual values. Analysis of our own and others' data indicates that this practice risks excluding 'Goldilocks' effects in which a biological variable falls within a range between 'too much' and 'too little' with a region between where its function is 'just right'; a concept captured by the Swedish term 'Lagom'. This was confirmed by a narrative search of the lit ...[more]

PMID: 31780584

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Project description:BackgroundMinimally invasive surgery had been tailored to individual cases of gastric subepithelial tumors (SETs) after comparing the clinical outcomes of endoscopic resection (ER), laparoscopic resection (LR), and hybrid methods.AimTo study the use of Goldilocks principle to determine the best form of minimally invasive surgery for gastric SETs.MethodsIn this retrospective study, 194 patients of gastric SETs with high probability of surgical intervention were included. All patients underwent tumor resection in the operating theater between January 2013 and December 2021. The patients were divided into two groups, ER or LR, according to the tumor characteristics and the initial intent of intervention. Few patients in the ER group required further backup laparoscopic surgery after an incomplete ER. The patients who had converted open surgery were excluded. A logistic regression model was used to assess the associations between patient characteristics and the likelihood of a treatment strategy. The area under the curve was used to assess the discriminative ability of tumor size and Youden's index to determine the optimal cut-off tumor size.ResultsOne-hundred ninety-four patients (100 in the ER group and 94 in the LR group) underwent tumor resection in the operating theater. In the ER group, 27 patients required backup laparoscopic surgery after an incomplete ER. The patients in the ER group had small tumor sizes and shorter procedure durations while the patients in the LR group had large tumor sizes, exophytic growth, malignancy, and tumors that were more often located in the middle or lower third of the stomach. Both groups had similar durations of hospital stays and a similar rate of major postoperative complications. The patients in the ER group who underwent backup surgery required longer procedures (56.4 min) and prolonged stays (2 d) compared to the patients in the LR group without the increased rate of major postoperative complications. The optimal cut-off point for the tumor size for laparoscopic surgery was 2.15 cm.ConclusionMultidisciplinary teamwork leads to the adoption of different strategies to yield efficient clinical outcomes according to the tumor characteristics.

Project description:Colorectal cancer is a common and deadly disease in the United States accounting for over 50,000 deaths in 2020. This progressive disease is highly preventable with early detection and treatment, but many people do not comply with the recommended screening guidelines. The gut microbiome has emerged as a promising target for noninvasive detection of colorectal cancer. Most microbiome-based classification efforts utilize taxonomic abundance data from operational taxonomic units (OTUs) or amplicon sequence variants (ASVs) with the goal of increasing taxonomic resolution. However, it is unknown which taxonomic resolution is optimal for microbiome-based classification of colorectal cancer. To address this question, we used a reproducible machine learning framework to quantify classification performance of models based on data annotated to phylum, class, order, family, genus, OTU, and ASV levels. We found that model performance increased with increasing taxonomic resolution, up to the family level where performance was equal (P > 0.05) among family (mean area under the receiver operating characteristic curve [AUROC], 0.689), genus (mean AUROC, 0.690), and OTU (mean AUROC, 0.693) levels before decreasing at the ASV level (P < 0.05; mean AUROC, 0.676). These results demonstrate a trade-off between taxonomic resolution and prediction performance, where coarse taxonomic resolution (e.g., phylum) is not distinct enough, but fine resolution (e.g., ASV) is too individualized to accurately classify samples. Similar to the story of Goldilocks and the three bears (L. B. Cauley, Goldilocks and the Three Bears, 1981), mid-range resolution (i.e., family, genus, and OTU) is "just right" for optimal prediction of colorectal cancer from microbiome data. IMPORTANCE Despite being highly preventable, colorectal cancer remains a leading cause of cancer-related death in the United States. Low-cost, noninvasive detection methods could greatly improve our ability to identify and treat early stages of disease. The microbiome has shown promise as a resource for detection of colorectal cancer. Research on the gut microbiome tends to focus on improving our ability to profile species and strain level taxonomic resolution. However, we found that finer resolution impedes the ability to predict colorectal cancer based on the gut microbiome. These results highlight the need for consideration of the appropriate taxonomic resolution for microbiome analyses and that finer resolution is not always more informative.

Dataset Information

Going to extremes: the Goldilocks/Lagom principle and data distribution.

Publications

Going to extremes: the Goldilocks/Lagom principle and data distribution.

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