scholarly journals Only seeing is believing – the power of evidence and reason

2016 ◽  
Vol 62 (3) ◽  
pp. 250-256
Author(s):  
Bernhard Rupp
Keyword(s):  
Prior Knowledge ◽  
Inductive Reasoning ◽  
Empirical Science ◽  
Evidence Based ◽  
Human Element ◽  
Scientific Epistemology ◽  
Biomolecular Crystallography ◽  
Mature Science ◽  
Physical Principles ◽  
Strong Claim

Biomolecular crystallography is a mature science that provides an instructive example for modern inductive reasoning as a model for Bayesian epistemology in empirical science. Fundamental scientific epistemology requires that a strong claim is supported by strong and convincing proof. Biomolecular crystallography, based on solid foundations of rich experimental data and extensive prior knowledge provides a prime example for modern, evidence based reasoning that strongly relies on assessments of plausibility based on prior knowledge while at the same time constantly delivering some of the most novel and exciting results based on new experimental evidence. As a consequence of the solid underlying physical principles and its mathematical rigor, crystallography as a mature science could be almost fool proof – were it not for the human element.

scholarly journals Employing Evidence: Does it Have a Job in Vocational Libraries?

2006 ◽  
Vol 1 (1) ◽  
pp. 26
Author(s):  
Cecily Martina ◽  
Bradley Jones
Keyword(s):  
Medical Education ◽  
Vocational Education ◽  
Allied Health ◽  
Empirical Science ◽  
Evidence Based ◽  
Objective Evidence ◽  
Science And Education ◽  
Competency Based ◽  
Professional Evaluation

Objective - Evidence based librarianship (EBL) springs from medical and academic origins. As librarians are tertiary educated (only occasionally with supplementary qualifications covering research and statistics) EBL has had an academic focus. The EBL literature has significant content from school and university perspectives, but has had little, if any, vocational content. This paper suggests a possible Evidence Based Librarianship context for vocational libraries. Methods - A multidisciplinary scan of evidence based literature was undertaken, covering medicine and allied health, librarianship, law, science and education. National and international vocational education developments were examined. The concept and use of evidence in vocational libraries was considered. Results - Library practice can generally benefit from generic empirical science methodologies used elsewhere. Different areas, however, may have different concepts of what constitutes evidence and appropriate methodologies. Libraries also need to reflect the evidence used in their host organisations. The Australian vocational librarian has been functioning in an evidence based educational sector: national, transportable, prescriptive, competency based and outcome driven Training Packages. These require a qualitatively different concept of evidence compared to other educational sectors as they reflect pragmatic, economic, employability outcomes. Conclusions - Vocational and other librarians have been doing research but need to be more systematic about design and analysis. Librarians need to develop ‘evidence literacy’ as one of their professional evaluation skills. Libraries will need to utilise evidence relevant to their host organisations to establish and maintain credibility, and in the vocational sector this is set in a competency based framework. Competency based measures are becoming increasingly relevant in school and university (including medical) education.

The Long Way From α-Error Control to Validity Proper

2012 ◽  
Vol 7 (6) ◽  
pp. 661-669 ◽  
Author(s):  
Klaus Fiedler ◽  
Florian Kutzner ◽  
Joachim I. Krueger
Keyword(s):  
Error Control ◽  
Behavioral Science ◽  
Statistical Tests ◽  
Inductive Reasoning ◽  
Empirical Science ◽  
Scientific Culture ◽  
Current State ◽  
Alternative Hypotheses ◽  
Survey Results ◽  
State Of Affairs

Several influential publications have sensitized the community of behavioral scientists to the dangers of inflated effects and false-positive errors leading to the unwarranted publication of nonreplicable findings. This issue has been related to prominent cases of data fabrication and survey results pointing to bad practices in empirical science. Although we concur with the motives behind these critical arguments, we note that an isolated debate of false positives may itself be misleading and counter-productive. Instead, we argue that, given the current state of affairs in behavioral science, false negatives often constitute a more serious problem. Referring to Wason’s (1960) seminal work on inductive reasoning, we show that the failure to assertively generate and test alternative hypotheses can lead to dramatic theoretical mistakes, which cannot be corrected by any kind of rigor applied to statistical tests of the focal hypotheses. We conclude that a scientific culture rewarding strong inference (Platt, 1964) is more likely to see progress than a culture preoccupied with tightening its standards for the mere publication of original findings.

scholarly journals How Scientism Infiltrated Medicine and Distorted Clinical Practice: Scientism May Be Hazardous to Your Health

2020 ◽  
Vol 3 (2) ◽  
pp. p117
Author(s):  
Charles J Kowalski ◽  
David Fessell ◽  
Adam J Mrdjenovich ◽  
Richard W Redman
Keyword(s):  
Clinical Practice ◽  
Evidence Based Medicine ◽  
Scientific Method ◽  
Human Learning ◽  
Empirical Science ◽  
Evidence Based ◽  
Universal Applicability ◽  
Based Medicine

Scientism can be defined as a passionate belief in the universal applicability of the scientific method and approach, and the view that empirical science constitutes the most authoritative worldview or most valuable part of human learning, to the exclusion of other viewpoints. At this level of generality, it is not difficult to show that scientism poses some distinct dangers, putting a damper as it does on the validity and usefulness of other kinds of knowledge and/or ways of learning. But this has not dissuaded some from thinking that scientism might still be of value in medicine. The popularity of evidence based medicine (EMB) attests to the fact that many so believe. We argue, to the contrary, that clinical practice relies on other kinds of knowledge, and that this is attainable only if we admit consideration of other kinds of learning. We conclude that scientism may be dangerous to your health.

scholarly journals The human intellect is the source of its own problems: Ligands of fancy

2014 ◽  
Vol 70 (a1) ◽  
pp. C1270-C1270
Author(s):  
Bernhard Rupp
Keyword(s):  
Electron Density ◽  
Complex Structure ◽  
Early Career ◽  
Important Work ◽  
Ligand Complex ◽  
Early Career Researchers ◽  
Scientific Epistemology ◽  
Empirical Reasoning ◽  
Human Intellect ◽  
Strong Claim

Fundamental scientific epistemology requires that a strong claim is supported by strong and convincing proof. As an example, the proposition of a ligand bound to a target protein in a precise conformation and pose presents a very strong claim, and confirming (positive omit difference) electron density will provide the correspondingly strong experimental evidence. A survey of ligand structure models has revealed that in an unexpectedly large number of ligand structures, the required electron density is not present or does not sufficiently support the proposed ligand pose. Upon detailed analysis, it emerged that the origin of such fanciful models lies in the simple neglect of the most basic fundamentals of scientific epistemology, which appears not to be formally taught in some science curricula. A brief introduction into the importance of evidence and its balance with prior expectations in a Bayesian system of empirical reasoning on hand of protein-ligand complex structure examples is therefore provided for young and aspiring scientists. It particular affects early career researchers when fictitious models intended to support bio-medically relevant hypotheses delay the progress of science by unsupported claims: grants cannot be funded when they contradict models believed to be true, and valuable taxpayer money can be squandered trying to pursue science based on false premises. Poor and at minimum self-deceptive work threatens to become systemic and accepted over the course of time unless young researches take full ownership and responsibility for their exciting and important work and resist any postmodern relativism threatening to erode the credibility of their profession.

scholarly journals Semantic Deep Learning: Prior Knowledge and a Type of Four-Term Embedding Analogy to Acquire Treatments for Well-Known Diseases (Preprint)

2019 ◽  
Author(s):  
Mercedes Arguello Casteleiro ◽  
Julio Des Diz ◽  
Nava Maroto ◽  
Maria Jesus Fernandez Prieto ◽  
Simon Peters ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prior Knowledge ◽  
Analogical Reasoning ◽  
Evidence Based ◽  
Semantic Field ◽  
Search Query ◽  
Disease Treatment ◽  
Search Queries ◽  
Semantic Fields ◽  
N Gram

BACKGROUND How to treat a disease remains to be the most common type of clinical question. Obtaining evidence-based answers from biomedical literature is difficult. Analogical reasoning with embeddings from deep learning (embedding analogies) may extract such biomedical facts, although the state-of-the-art focuses on pair-based proportional (pairwise) analogies such as man:woman::king:queen (“<i>queen = −man +king +woman</i>”). OBJECTIVE This study aimed to systematically extract disease treatment statements with a Semantic Deep Learning (SemDeep) approach underpinned by prior knowledge and another type of 4-term analogy (other than pairwise). METHODS As preliminaries, we investigated Continuous Bag-of-Words (CBOW) embedding analogies in a common-English corpus with five lines of text and observed a type of 4-term analogy (not pairwise) applying the 3CosAdd formula and relating the semantic fields <i>person</i> and <i>death</i>: “dagger = −Romeo +die +died” (search query: −<i>Romeo +die +died</i>). Our SemDeep approach worked with pre-existing items of knowledge (what is known) to make inferences sanctioned by a 4-term analogy (search query −<i>x +z1 +z2</i>) from CBOW and Skip-gram embeddings created with a PubMed systematic reviews subset (PMSB dataset). Stage1: Knowledge acquisition. Obtaining a set of terms, candidate y, from embeddings using vector arithmetic. Some n-gram pairs from the cosine and validated with evidence (prior knowledge) are the input for the 3cosAdd, seeking a type of 4-term analogy relating the semantic fields disease and treatment. Stage 2: Knowledge organization. Identification of candidates sanctioned by the analogy belonging to the semantic field treatment and mapping these candidates to unified medical language system Metathesaurus concepts with MetaMap. A concept pair is a brief disease treatment statement (biomedical fact). Stage 3: Knowledge validation. An evidence-based evaluation followed by human validation of biomedical facts potentially useful for clinicians. RESULTS We obtained 5352 n-gram pairs from 446 search queries by applying the 3CosAdd. The microaveraging performance of MetaMap for candidate <i>y</i> belonging to the semantic field <i>treatment</i> was F-measure=80.00% (precision=77.00%, recall=83.25%). We developed an empirical heuristic with some predictive power for <i>clinical winners</i>, that is, search queries bringing candidate <i>y</i> with evidence of a therapeutic intent for target disease <i>x</i>. The search queries <i>-asthma +inhaled_corticosteroids +inhaled_corticosteroid</i> and <i>-epilepsy +valproate +antiepileptic_drug</i> were <i>clinical winners</i>, finding eight evidence-based beneficial treatments. CONCLUSIONS Extracting treatments with therapeutic intent by analogical reasoning from embeddings (423K n-grams from the PMSB dataset) is an ambitious goal. Our SemDeep approach is knowledge-based, underpinned by embedding analogies that exploit prior knowledge. Biomedical facts from embedding analogies (4-term type, not pairwise) are potentially useful for clinicians. The heuristic offers a practical way to discover beneficial treatments for well-known diseases. Learning from deep learning models does not require a massive amount of data. Embedding analogies are not limited to pairwise analogies; hence, analogical reasoning with embeddings is underexploited.

Bellagio II Report: Terrestrial Applications of Space Medicine Research

2021 ◽  
Vol 92 (8) ◽  
pp. 650-669
Author(s):  
Marian B. Sides ◽  
Smith L. Johnston ◽  
Adam Sirek ◽  
Peter H. Lee ◽  
Rebecca S. Blue ◽  
...  
Keyword(s):  
Space Station ◽  
National Institutes Of Health ◽  
Evidence Based ◽  
Space Medicine ◽  
Space Applications ◽  
Medicine Research ◽  
Science Models ◽  
Long Duration ◽  
Physiological Adaptations ◽  
Mature Science

AbstractINTRODUCTION: For over 50 yr, investigators have studied the physiological adaptations of the human system during short- and long-duration spaceflight exposures. Much of the knowledge gained in developing health countermeasures for astronauts onboard the International Space Station demonstrate terrestrial applications. To date, a systematic process for translating these space applications to terrestrial human health has yet to be defined.METHODS: In the summer of 2017, a team of 38 international scientists launched the Bellagio ll Summit Initiative. The goals of the Summit were: 1) To identify space medicine findings and countermeasures with highest probability for future terrestrial applications; and 2) To develop a roadmap for translation of these countermeasures to future terrestrial application. The team reviewed public domain literature, NASA databases, and evidence books within the framework of the five-stage National Institutes of Health (NIH) translation science model, and the NASA two-stage translation model. Teams then analyzed and discussed interdisciplinary findings to determine the most significant evidence-based countermeasures sufficiently developed for terrestrial application.RESULTS: Teams identified published human spaceflight research and applied translational science models to define mature products for terrestrial clinical practice.CONCLUSIONS: The Bellagio ll Summit identified a snapshot of space medicine research and mature science with the highest probability of translation and developed a Roadmap of terrestrial application from space medicine-derived countermeasures. These evidence-based findings can provide guidance regarding the terrestrial applications of best practices, countermeasures, and clinical protocols currently used in spaceflight.Sides MB, Johnston SL III, Sirek A, Lee PH, Blue RS, Antonsen EL, Basner M, Douglas GL, Epstein A, Flynn-Evans EE, Gallagher MB, Hayes J, Lee SMC, Lockley SW, Monseur B, Nelson NG, Sargsyan A, Smith SM, Stenger MB, Stepanek J, Zwart SR; Bellagio II Team. Bellagio II report: terrestrial applications of space medicine research. Aerosp Med Hum Perform. 2021; 92(8):650669.

Discovery of physical principles from design experiences

1994 ◽  
Vol 8 (2) ◽  
pp. 113-123 ◽  
Author(s):  
Sambasiva R. Bhatta ◽  
Ashok K. Goel
Keyword(s):  
Hypothesis Testing ◽  
Prior Knowledge ◽  
Causal Explanation ◽  
Hypothesis Formation ◽  
Behavior Function ◽  
Independent Behavior ◽  
Physical Principles

AbstractOne method for making analogies is to access and instantiate abstract domain principles, and one method for acquiring knowledge of abstract principles is to discover them from experience. We view generalization over experiences in the absence of any prior knowledge of the target principle as the task of hypothesis formation, a subtask of discovery. Also, we view the use of the hypothesized principles for analogical design as the task of hypothesis testing, another subtask of discovery. In this paper, we focus on discovery of physical principles by generalization over design experiences in the domain of physical devices. Some important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. We represent a reasoner's comprehension of specific designs in the form of structure-behavior-function (SBF) models. An SBF model provides a functional and causal explanation of the working of a device. We represent domain principles as device-independent behavior-function (BF) models. We show that (1) the function of a device determines what to generalize from its SBF model, (2) the SBF model itself suggests how far to generalize, and (3) the typology of functions indicates what method to use.

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