Many clinically relevant forms of acute injury, such as stroke, traumatic brain injury, and myocardial infarction, have resisted treatments to prevent cell death following injury. The clinical failures can be linked to the currently used inductive models based on biological specifics of the injury system. Here we contrast the application of inductive and deductive models of acute cell injury. Using brain ischemia as a case study, we discuss limitations in inductive inferences, including the inability to unambiguously assign cell death causality and the lack of a systematic quantitative framework. These limitations follow from an overemphasis on qualitative molecular pathways specific to the injured system.
The biomedical problem is can the neuron Traditional deductive model steps be prevented? Qualitative and Quantative approaches You gave clear differences in a balance, simple to understand, I suppose you are a teacher by profession. DeGracia: ude. My question Bet uncut models this: Can online survey questionnaire be used with the inductive approach? This standardization on specific parameter values was predicated on the development of animal models of brain ischemia which mimic features of human brain ischemia [ 3233 ]. Fornace A. The steps between overactivating ER function or translation and the setps disintegration of a cell are unknown. Myerburg R.
Alibi lawrence lyric tracy. Inductive Research Approach
Unanswered Questions. Robert Ellrich Pokerace: Thanks for sharing your thoughts about how to market your bu. The Traditional deductive model steps approach contrasts with other research models such as the inductive approach or grounded theory. A relative comparison is made by Smurfs midi each criteria with another. The modern approach is mostly based on new age methods and beliefs. Asked in Scientific Method Does scientific method use inductive or deductive reasoning? A false hypothesis does not necessarily mean that the area of research is now closed or incorrect. Benson and trans. Asked in Science Experiments How do you make a model of the heart for science deduchive Theory formulation4. Asked in Scientific Method. Philosophy of science. The Research Council of Norway.
Abductive reasoning, also referred to as abductive approach is set to address weaknesses associated with deductive and inductive approaches.
- All managers face issues every day that need decisions; decisions about managing employees, resources and setting plans and strategies.
- If a causal relationship or link seems to be implied by a particular theory or case example, it might be true in many cases.
- The hypothetico-deductive model or method is a proposed description of scientific method.
- The hypothetico-deductive method is one of the mainstays of scientific research, often regarded as the only 'true' scientific research method.
Many clinically relevant forms of acute injury, such as stroke, traumatic brain injury, and myocardial infarction, have resisted treatments to prevent cell death following injury.
The clinical failures can be linked to the currently used inductive models based on biological specifics of the injury system. Here we contrast the application of inductive and deductive models of acute cell injury.
Using brain ischemia as a case study, we discuss limitations in inductive inferences, including the inability to unambiguously assign cell death causality and the lack of a systematic quantitative framework. These limitations follow from an overemphasis on qualitative molecular pathways specific to the injured system. Our recently developed nonlinear dynamical theory of cell injury provides a generic, systematic approach to cell injury in which attractor states and system parameters are used to quantitatively characterize acute injury systems.
The theoretical, empirical, and therapeutic implications of shifting to a deductive framework are discussed. We illustrate how a deductive mathematical framework offers tangible advantages over qualitative inductive models for the development of therapeutics of acutely injured biological systems. In spite of decades of intensive research and hundreds of clinical trials, clinically important, life-threatening forms of acute injury have stubbornly resisted successful therapeutic intervention.
Two notable examples are ischemia of the brain [ 1 ] and ischemia of the heart [ 2 ]. The former manifests clinically during stroke and cardiac arrest and the latter during myocardial infarction.
The clinical trial failures have prompted three main responses: 1 some have closely scrutinized technical details of preclinical and clinical research [ 3 , 4 ]; 2 others have advocated for additional basic research [ 5 ]; 3 still others have suggested therapeutics may not be possible for a given type of injury, such as stroke [ 6 ].
The first option focuses on analytical techniques, the second emphasizes empirical information, and the third blurs the distinction between the science of acute injury and its technological application in the form of therapy.
The area given the least consideration in the literature evaluating clinical trial failures is the theoretical foundation [ 1 ]. We recently developed a nonlinear dynamical theory of acute cell injury [ 7 ] that suggests that an important aspect of clinical trials failure can be attributed to inadequate theory.
Our purpose here is to discuss that the theoretical problems can fruitfully be encapsulated by the classical distinction between inductive and deductive reasoning. The nonlinear cell injury model is a deductive framework for understanding cellular injury in a generic fashion. The approaches used in mainstream biomedicine are inductive and based on the biological specifics of the injury system under study.
However, inductive phenomenological approaches do not allow a strict attribution of causality. Without a clear concept of cell death causality, how is therapy, the prevention of cell death, supposed to be carried out?
Further, inductively studying phenomenology fosters a qualitative mind-set that neglects systematic quantitative considerations. Clinical injuries occur across a range of injury magnitudes and cell responses are generally graded [ 8 ]. Thus, how are cellular responses, causal mechanisms, and outcome to be linked in a quantitative fashion to injury magnitude?
Inductive and deductive models possess different implications for conceptualizing causal mechanisms and quantification of injury states. We build the case that a deductive, mathematical framework offers tangible advantages over qualitative inductive models for the development of therapeutics of acutely injured biological systems. Here we briefly discuss the strengths and weaknesses of induction and deduction.
Induction reasons from the specific to the general. Many specific cases are observed, and from these general conclusions are drawn. Deduction is the opposite of induction. With deduction one begins with general premises and from these deduces, or derives, specific cases.
Both forms of reasoning are used in modern science, but their differences have long bothered commentators of science. There is asymmetry between induction and deduction with respect to preserving the truth value of propositions.
Deductive reasoning explicitly exposes the logical steps and thereby preserves the truth value from premises to conclusions. The canonical example of deduction is the derivation of a theorem from a set of axioms in mathematics. Unlike deduction, there cannot be a finite, explicit series of logical steps associated with an inductive inference because these are always based on open-ended sets, such as extrapolating from past to future events, or from observed cases to unobserved cases.
In general, deduction is used in mathematics and physics where premises can be expressed mathematically. Then, derivation of theorems or solutions of equations reveal consequences that follow inevitably from the premises. A weakness of the scientific application of deduction is that the axioms must accurately represent the system under study.
If the axioms are incorrect so will be the conclusions, even if the intervening steps are logically sound. This weakness is offset by the flexibility with which one can alter the axioms and rederive new conclusions accordingly. An example of successful deduction is Einstein's special theory of relativity that is based on only two axioms [ 9 ]: 1 the laws of physics are the same for all observers in inertial reference frames, and 2 the speed of light in a vacuum is constant.
Both axioms are expressible mathematically, and the resulting deductions, such as length contraction at high velocities, have been successfully verified by experiment [ 10 ]. Sciences seeking to explain complex phenomena, such as biology or sociology, have traditionally utilized induction more than deduction. One example of the application of inductive logic in biology is Darwin's theory of evolution by natural selection [ 11 ].
This theory, or model, was not derived from axiomatic principles but instead was a generalization drawn from many specific pieces of evidence: comparative anatomy, comparative ethology, Darwin's field observations in the Galapagos Islands, domestic breeding experience, and other evidence went into the inductive inference that species arise by natural selection. Given the generic weaknesses of induction, it is interesting to note that deductive alternatives to explaining biological form are emerging in biology [ 12 ].
The analysis of induction is associated with the work of the philosopher David Hume. Hume demonstrated that deduction cannot be used to explicitly prove the truth value of an inductive inference. The inductive inference to be proved must be taken as an axiom, thereby leading to circular logic between the premise and conclusion [ 13 ]. Thus, inductive inferences are open-ended, and acting upon them requires faith that no contradictory case will eventually appear.
An important implication of inductive inference is that it does not, strictly speaking, allow attribution of causality. This conclusion has bothered philosophers of science who have sought to wiggle out of Hume's problem of induction. A summary of philosophy of science views on induction [ 14 ] is certainly beyond our present scope, so here we mention only two salient ideas on this intellectual landscape.
Karl Popper suggested that science can avoid the weaknesses of induction by combining observation and deduction in the quest to falsify hypotheses [ 15 ]. In this view, confirmation or verification i. Popper's thinking underlies, for example, the practice of disproving the null hypothesis in statistics. However, Popper's view was superseded by Kuhn's model of scientific paradigms as complex webs of belief that cannot easily be shoehorned into the classical distinction of inductive versus deductive reasoning [ 16 ].
The net result is that the use of induction in science has not been resolved and remains an open question. However, continuing advances in mathematics, physics, and the understanding of complex systems have expanded the purview of deductive logic into traditionally inductive realms, including biology and the corollary study of biology, biomedicine.
Before discussing a deductive approach to biomedicine, we illustrate the prevailing inductive biomedical approach using the field of brain ischemia as an example. While we use brain ischemia as our case study of inductive biomedicine, the template we derive below holds for other biomedical fields such as heart ischemia, traumatic brain injury, epilepsy, diabetes, acute kidney injury, cancer, and other complex disease states.
The scientific problem is how does ischemia cause neurons to die? The biomedical problem is can the neuron death be prevented? There is massive clinical experience with brain ischemia in the forms of stroke and cardiac arrest and resuscitation. Stroke affects approximately three-quarter million people in the USA each year and is the fourth leading cause of death [ 17 ]. Cardiac arrest stops all blood flow to the brain and is an example of global brain ischemia.
Thus, brain ischemia results in substantial mortality and morbidity. It is no surprise that this clinical challenge has been met with a proportional research response. These tremendous research efforts have had positive impact at the front end of the disease process: incidence of heart disease and stroke has progressively decreased over the past decade [ 24 ].
However, at the back end, after a patient has suffered brain ischemia, all the research to present has produced minimal clinically tangible benefit. Physicians have only limited options to treat ischemia-induced neuron death in stroke and cardiac arrest patients e. Therefore, with respect to preventing neurological damage after the injury had occurred, it is fair to conclude that the amount of research invested over the past decades has not produced a proportional return on investment.
We suggest that a key factor is that brain ischemia research has been theoretically rudderless. To build this case, we first summarize the salient points in the evolution of the field to illustrate its inductive logic. The earliest approaches to brain ischemia studies were empirically systematic but were not grounded in any deductive framework. Thus, neuron death was discovered to be dependent on 1 the degree and 2 the duration of blood flow reduction.
Both factors are, by current standards, system parameters, but the field did not evolve in the quantitative direction implied by this insight. Because there are so many possible combinations of blood flow reduction and time, it is impossible to empirically evaluate all of them.
Thus, the field has standardized on specific blood flow reduction and time increments. This standardization on specific parameter values was predicated on the development of animal models of brain ischemia which mimic features of human brain ischemia [ 32 , 33 ]. Via the animal models, stroke researchers identified two forms of ischemia-induced neuron death: necrosis of tissue in the ischemic epicenter and a delayed form of neuron death in a penumbral volume around the epicenter [ 34 — 36 ].
Studies of global brain ischemia, modeling cardiac arrest, followed a similar progression. In the s, Kirino discovered that specific durations of global brain ischemia caused a delayed neuronal death of a specific subtype of hippocampal neurons CA1 layer days after the ischemia [ 34 , 37 ].
Again, the field standardized on ischemia conditions causing CA1 death and focused on the question: what causes the CA1 neurons to die in a delayed fashion days after global brain ischemia? In summary, the original studies of brain ischemia began in a reasonably systematic fashion studying the effects of ranges of brain blood flow and duration.
But as the field matured, the experimental animal model systems became standardized around only a few parameter combinations of brain blood flow and duration. These were never conceptualized as system parameters in a formal deductive sense but instead evolved into the rote procedure for conducting the experimental animal models. The animal models have been used to dissect brain tissue after ischemia to investigate how it changes compared to the nonischemic brain.
The observed phenomenological differences became conflated with causation. Before detailing phenomenological studies, we summarize the therapeutic expectation for understanding how ischemia causes neurons to die. The brain is completely dependent on blood flow for making ATP and has little or no backup reserves of glucose [ 38 ]. Thus, the proximal damage event in brain ischemia is rapid loss of ATP. Clinically, this gives rise to two treatment possibilities: 1 reperfusion therapies and 2 neuroprotection.
Therefore, reperfusions therapies seek to rapidly regain blood flow and thereby reduce the ischemia as quickly as possible to minimize loss of ATP [ 40 ]. Reperfusion therapies can be administered surgically or chemically with tPA and are used clinically [ 41 ]. However, reperfusion therapies can only be applied under limited circumstances. In the majority of cases, the patient experiences brain ischemia in a setting where there is no possibility of performing a reperfusion therapy.
In such cases the ischemia is unavoidable and occurs for some duration before the patient receives medical attention. In the case of stroke, this may be 24 hours after the ischemic event has occurred [ 42 ].
If the predictions are correct, then the hypothesis is confirmed. Theory hypotheses observation confirmation. Asked in Political Science What Traditional and modern approaches to political science? Psychology as a traditional belief is base on traditions and supertitions while Psychology as a science uses measurement techniques to study sensation. If a causal relationship or link seems to be implied by a particular theory or case example, it might be true in many cases.
Traditional deductive model steps. Related Questions
Abductive reasoning (abductive approach) - Research-Methodology
The hypothetico-deductive method is one of the mainstays of scientific research, often regarded as the only 'true' scientific research method. This area fuels intense debate and discussion between many fields of scientific specialization. Concisely, the method involves the traditional steps of observing the subject, in order to elaborate upon an area of study. This allows the researcher to generate a testable and realistic hypothesis. The hypothesis must be falsifiable by recognized scientific methods but can never be fully confirmed, because refined research methods may disprove it at a later date.
From the hypothesis , the researcher must generate some initial predictions, which can be proved, or disproved, by the experimental process. These predictions must be inherently testable for the hypothetico-deductive method to be a valid process. For example, trying to test the hypothesis that God exists would be difficult, because there is no scientific way to evaluate it. The next stage is to perform the experiment , obtaining statistically testable results , which can be used to analyze the results and determine whether the hypothesis has validity or has little foundation.
This experiment must involve some manipulation of variables to allow the generation of analyzable data. Finally, statistical tests will confirm whether the predictions are correct or not. This method is usually so rigorous that it is rare for a hypothesis to be completely proved, but some of the initial predictions may be correct and will lead to new areas of research and refinements of the hypothesis. Proving and confirming a hypothesis is never a clear-cut and definitive process.
Statistics is a science based on probability, and however strong the results generated; there is always a chance of experimental error. In addition, there may be another unknown reason that explains the results.
Most theories, however solid the proof, develop and evolve over time, changing and adapting as new research refines the known data. Proving a hypothesis is never completely accurate but, after a process of debate and retesting of the results, may become a scientific assumption. Science is built upon these ' paradigms ' and even commonly accepted views may prove to be inaccurate upon further exploration. A false hypothesis does not necessarily mean that the area of research is now closed or incorrect.
The experiment may not have been accurate enough, or there may have been some other contributing error. This is why the hypothetico-deductive method relies on initial predictions; very few hypotheses, if the research is thorough, are completely wrong as they generate new directions for future research. Check out our quiz-page with tests about:. Martyn Shuttleworth Oct 10, Hypothetico-Deductive Method. Retrieved Oct 19, from Explorable. The text in this article is licensed under the Creative Commons-License Attribution 4.
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