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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">Richard Feynman&#44; winner of the Nobel Prize in Physics&#44; began one of his Caltech lectures with the following statement&#58; &#8220;The exception proves that the rule is wrong&#46; If there is an exception to any rule&#44; and if it can be proved by observation&#44; that rule is wrong&#46;&#8221;</p><p id="par0010" class="elsevierStylePara elsevierViewall">There are two facets to scientific research&#58; the first&#44; the descriptive&#44; is based on the closest possible observation and description of a certain phenomenon&#59; the second consists of explaining that observation&#46; The ideal explanation comes in the form of a mathematical formula that accurately predicts what will happen each time the phenomenon occurs&#46; In 1905&#44; Albert Einstein published the most famous formula of all time&#44; his theory of relativity&#44; barely understandable to more than a select handful of physicists&#46; How could anyone conceive that a human body could contain as much energy as a city would use in a year&#63; And Einstein received his Nobel for his explanation of the photoelectric effect&#44; not for his devastating discovery of relativity&#46; All laboratory attempts to disprove Einstein have failed&#46; An experiment set up in 2011 in the CERN&#44; the European Organization for Nuclear Research&#44; initially appeared to undo the theory&#44; but&#44; after the initial excitement&#44; it was confirmed in 2012 that there was an error in calculation&#58; Einstein and relativity had survived another coup attempt&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">Medicine is&#44; in general&#44; a probabilistic science&#46; Most of the phenomena studied are affected by a large number of variables&#59; usually we are aware of only a few&#44; and very little is known about the role these few play in the development of the phenomenon in question&#46; For example&#44; the volume of air exhaled in one second&#8211; FEV1&#8211;is subject to probably hundreds&#44; or maybe even thousands&#44; of variables&#46; This volume is measured in a large number of subjects and a somewhat inelegant equation is formulated&#46; The level of accuracy of the formula is proven when it is applied to large numbers of individuals&#46; Most of the regression equations we use for disease processes are not sufficiently precise&#46; For example&#44; it is impossible to quantify the probability of a certain individual developing a 4<span class="elsevierStyleHsp" style=""></span>cm pulmonary cavity in the upper left lobe after 24<span class="elsevierStyleHsp" style=""></span>hours in contact with a tuberculosis patient&#46; For this reason&#44; it is essential that clinicians clearly understand what they are saying when they speak of values in the context of a specific situation&#46; For a start&#44; we need to understand that medicine is basically a descriptive science&#46; Experiments &#40;clinical trials&#44; for example&#41; are performed&#44; we record what happens and extract some conclusions&#46; The reliability of these conclusions depends largely on the precision of the tools used for measurement&#46; In physics&#44; an experiment can be performed in a single case&#44; because the number of variables is small and usually can be controlled&#46; If an object falls to Earth from a height of 20 meters&#44; it will always reach the same acceleration and velocity&#44; exactly as predicted by a simple formula&#46; So what happens if we try to study a bronchodilator in COPD patients&#63; The study parameters are usually lung function &#40;FEV1&#41;&#44; quality of life &#40;questionnaires&#41;&#44; or maybe the number and type of exacerbations&#46; In a study carried out in humans&#44; these parameters are subject to an infinite variety of intra-individual variables&#44; most of which are impossible to define or to control&#46; These subject variables unavoidably affect the primary variable &#8211; the subject&#39;s state of mind alone during the test introduces an indefinite number of new factors&#46; The accuracy of the tools used to measure each parameter varies widely&#58; FEV1 is reasonably precise&#44; but quality of life&#44; being only semi-quantitative&#44; is less so&#46; As for exacerbations&#44; we do not even have a precise definition confirming with 100&#37; certainty whether an event was or was not an exacerbation&#44; not to mention quantifying the exacerbation any further than a simple classification&#46; Returning to FEV1&#44; we perform at least 3 tests&#44; accurate to the nearest millimeter&#44; but the chances are that each result will be different&#46; The ideal situation would be to perform the test many&#44; many times over and take the most frequently repeated result&#44; but this would be impossible from a practical point of view&#46; According to SEPAR criteria&#44;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">1</span></a> the highest value should be used&#44; provided the difference between the 2 best results is less than 150<span class="elsevierStyleHsp" style=""></span>ml &#40;100<span class="elsevierStyleHsp" style=""></span>ml if FEV1 is less than 1000<span class="elsevierStyleHsp" style=""></span>ml&#41;&#46; It seems inappropriate to set an absolute number in a test with a range of normal values than can be more than doubled&#44; depending on the individual&#46; Even if a variation of 10&#37; is accepted&#44; the test is still far from precise&#46; Mean improvement in FEV1 with the most potent bronchodilators available is about 170<span class="elsevierStyleHsp" style=""></span>ml&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">2</span></a> When 2 bronchodilators are compared&#44; the difference ranges from 30<span class="elsevierStyleHsp" style=""></span>ml to 90<span class="elsevierStyleHsp" style=""></span>ml&#46;<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">3&#44;4</span></a> It seems obvious that if the increase is much lower than the value considered acceptable for test reproducibility&#44; the results will be less reliable&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">As mentioned above&#44; in physics a principle needs to be based on mathematical proof that can accurately predict the results of any test based on that principle&#46; In the case of FEV1&#44; the range of possible values is wide&#46; For this reason&#44; we usually express the value as s mean of the total range&#46; To apply these results to a specific patient&#44; then&#44; is practically impossible&#46; We cannot predict what will happen if the patient receives such and such bronchodilator&#44; any more than we can predict the income of an individual person from the average income of their country&#46; It would seem&#44; then&#44; more effective to determine the change that would have positive consequences on the patient&#39;s health and determine how many patients in each trial group achieve it&#46; If the difference is 15&#37; in favor of the drug&#44; perhaps that percentage comes close to the real change that the patient would need to demonstrate to achieve improvement&#46; The problem is to accurately determine the minimum value of improvement&#46; In respiratory medicine&#44; the threshold limits are insufficiently precise&#46; Values of 100<span class="elsevierStyleHsp" style=""></span>ml have been proposed for FEV1&#44;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">5</span></a> although it seems more reasonable to establish percentage changes&#46; The St&#46; George&#39;s Hospital Questionnaire sets the change at 4&#37;&#44; but does this have the same relevance if the baseline score is 35 or 58&#63; What is the practical value of a mean change of 2&#46;5&#37; compared to a reference agent&#44; even if the difference according to the statistical analysis is very significant&#63; Is this information of any help to our patient&#63;</p><p id="par0025" class="elsevierStylePara elsevierViewall">Medicine is a probabilistic science that often only addresses events that are difficult to explain or understand&#46; Study designs must be closely analyzed and conclusions must be drawn with care&#46; The Heisenberg uncertainty principle determines that any measurement will be affected by the measurement system used&#46; When dealing with probabilities&#44; we must be very aware of what we are measuring&#44; how we measure it and to what or to whom the measurement can be applied&#46; Einstein said it was crazy to repeat a test again and again&#44; expecting to obtain different results&#46; In medicine&#44; studies that are similar &#40;or so we think&#41; in design&#44; often produce very different results&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">To conclude&#44; it might be a good idea to apply some of the principles of physics to medical research&#44; so that the findings from clinical trials can be better measured and evaluated&#46;</p></span>"
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Editorial
The Exception does not Prove the Rule: Lessons From Physics
La excepción no confirma la regla: lecciones de la física
Adolfo Baloira Villar
Corresponding author
adolfo.baloira.villar@sergas.es

Corresponding author.
, Marta Núñez Fernández
Servicio de Neumología, Complejo Hospitalario Universitario de Pontevedra, Pontevedra, Spain
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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">Richard Feynman&#44; winner of the Nobel Prize in Physics&#44; began one of his Caltech lectures with the following statement&#58; &#8220;The exception proves that the rule is wrong&#46; If there is an exception to any rule&#44; and if it can be proved by observation&#44; that rule is wrong&#46;&#8221;</p><p id="par0010" class="elsevierStylePara elsevierViewall">There are two facets to scientific research&#58; the first&#44; the descriptive&#44; is based on the closest possible observation and description of a certain phenomenon&#59; the second consists of explaining that observation&#46; The ideal explanation comes in the form of a mathematical formula that accurately predicts what will happen each time the phenomenon occurs&#46; In 1905&#44; Albert Einstein published the most famous formula of all time&#44; his theory of relativity&#44; barely understandable to more than a select handful of physicists&#46; How could anyone conceive that a human body could contain as much energy as a city would use in a year&#63; And Einstein received his Nobel for his explanation of the photoelectric effect&#44; not for his devastating discovery of relativity&#46; All laboratory attempts to disprove Einstein have failed&#46; An experiment set up in 2011 in the CERN&#44; the European Organization for Nuclear Research&#44; initially appeared to undo the theory&#44; but&#44; after the initial excitement&#44; it was confirmed in 2012 that there was an error in calculation&#58; Einstein and relativity had survived another coup attempt&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">Medicine is&#44; in general&#44; a probabilistic science&#46; Most of the phenomena studied are affected by a large number of variables&#59; usually we are aware of only a few&#44; and very little is known about the role these few play in the development of the phenomenon in question&#46; For example&#44; the volume of air exhaled in one second&#8211; FEV1&#8211;is subject to probably hundreds&#44; or maybe even thousands&#44; of variables&#46; This volume is measured in a large number of subjects and a somewhat inelegant equation is formulated&#46; The level of accuracy of the formula is proven when it is applied to large numbers of individuals&#46; Most of the regression equations we use for disease processes are not sufficiently precise&#46; For example&#44; it is impossible to quantify the probability of a certain individual developing a 4<span class="elsevierStyleHsp" style=""></span>cm pulmonary cavity in the upper left lobe after 24<span class="elsevierStyleHsp" style=""></span>hours in contact with a tuberculosis patient&#46; For this reason&#44; it is essential that clinicians clearly understand what they are saying when they speak of values in the context of a specific situation&#46; For a start&#44; we need to understand that medicine is basically a descriptive science&#46; Experiments &#40;clinical trials&#44; for example&#41; are performed&#44; we record what happens and extract some conclusions&#46; The reliability of these conclusions depends largely on the precision of the tools used for measurement&#46; In physics&#44; an experiment can be performed in a single case&#44; because the number of variables is small and usually can be controlled&#46; If an object falls to Earth from a height of 20 meters&#44; it will always reach the same acceleration and velocity&#44; exactly as predicted by a simple formula&#46; So what happens if we try to study a bronchodilator in COPD patients&#63; The study parameters are usually lung function &#40;FEV1&#41;&#44; quality of life &#40;questionnaires&#41;&#44; or maybe the number and type of exacerbations&#46; In a study carried out in humans&#44; these parameters are subject to an infinite variety of intra-individual variables&#44; most of which are impossible to define or to control&#46; These subject variables unavoidably affect the primary variable &#8211; the subject&#39;s state of mind alone during the test introduces an indefinite number of new factors&#46; The accuracy of the tools used to measure each parameter varies widely&#58; FEV1 is reasonably precise&#44; but quality of life&#44; being only semi-quantitative&#44; is less so&#46; As for exacerbations&#44; we do not even have a precise definition confirming with 100&#37; certainty whether an event was or was not an exacerbation&#44; not to mention quantifying the exacerbation any further than a simple classification&#46; Returning to FEV1&#44; we perform at least 3 tests&#44; accurate to the nearest millimeter&#44; but the chances are that each result will be different&#46; The ideal situation would be to perform the test many&#44; many times over and take the most frequently repeated result&#44; but this would be impossible from a practical point of view&#46; According to SEPAR criteria&#44;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">1</span></a> the highest value should be used&#44; provided the difference between the 2 best results is less than 150<span class="elsevierStyleHsp" style=""></span>ml &#40;100<span class="elsevierStyleHsp" style=""></span>ml if FEV1 is less than 1000<span class="elsevierStyleHsp" style=""></span>ml&#41;&#46; It seems inappropriate to set an absolute number in a test with a range of normal values than can be more than doubled&#44; depending on the individual&#46; Even if a variation of 10&#37; is accepted&#44; the test is still far from precise&#46; Mean improvement in FEV1 with the most potent bronchodilators available is about 170<span class="elsevierStyleHsp" style=""></span>ml&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">2</span></a> When 2 bronchodilators are compared&#44; the difference ranges from 30<span class="elsevierStyleHsp" style=""></span>ml to 90<span class="elsevierStyleHsp" style=""></span>ml&#46;<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">3&#44;4</span></a> It seems obvious that if the increase is much lower than the value considered acceptable for test reproducibility&#44; the results will be less reliable&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">As mentioned above&#44; in physics a principle needs to be based on mathematical proof that can accurately predict the results of any test based on that principle&#46; In the case of FEV1&#44; the range of possible values is wide&#46; For this reason&#44; we usually express the value as s mean of the total range&#46; To apply these results to a specific patient&#44; then&#44; is practically impossible&#46; We cannot predict what will happen if the patient receives such and such bronchodilator&#44; any more than we can predict the income of an individual person from the average income of their country&#46; It would seem&#44; then&#44; more effective to determine the change that would have positive consequences on the patient&#39;s health and determine how many patients in each trial group achieve it&#46; If the difference is 15&#37; in favor of the drug&#44; perhaps that percentage comes close to the real change that the patient would need to demonstrate to achieve improvement&#46; The problem is to accurately determine the minimum value of improvement&#46; In respiratory medicine&#44; the threshold limits are insufficiently precise&#46; Values of 100<span class="elsevierStyleHsp" style=""></span>ml have been proposed for FEV1&#44;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">5</span></a> although it seems more reasonable to establish percentage changes&#46; The St&#46; George&#39;s Hospital Questionnaire sets the change at 4&#37;&#44; but does this have the same relevance if the baseline score is 35 or 58&#63; What is the practical value of a mean change of 2&#46;5&#37; compared to a reference agent&#44; even if the difference according to the statistical analysis is very significant&#63; Is this information of any help to our patient&#63;</p><p id="par0025" class="elsevierStylePara elsevierViewall">Medicine is a probabilistic science that often only addresses events that are difficult to explain or understand&#46; Study designs must be closely analyzed and conclusions must be drawn with care&#46; The Heisenberg uncertainty principle determines that any measurement will be affected by the measurement system used&#46; When dealing with probabilities&#44; we must be very aware of what we are measuring&#44; how we measure it and to what or to whom the measurement can be applied&#46; Einstein said it was crazy to repeat a test again and again&#44; expecting to obtain different results&#46; In medicine&#44; studies that are similar &#40;or so we think&#41; in design&#44; often produce very different results&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">To conclude&#44; it might be a good idea to apply some of the principles of physics to medical research&#44; so that the findings from clinical trials can be better measured and evaluated&#46;</p></span>"
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ISSN: 15792129
Original language: English
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