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Why several truths can be true

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ABSTRACT

In this paper, we offer a perspective on complementarity, acknowledging that it is not possible for human perception and cognition to grasp reality with unambiguous concepts or theories. Therefore, multiple concepts and perspectives are valid when they are not exaggerated beyond reasonable limits and do not claim exclusive validity. We recommend a humble stance enabling respectful dialogue between different perspectives in medical science and practice.

Key points: No single perspective in clinical or scientific medicine can exhaustively explain medical phenomena.

Key points: Scientific attitude is characterised by a willingness to look for objections against what we prefer as truths.

Key points: Complementarity or unifying contradictions are concepts that allow for humility and pluralism in clinical and scientific medicine.

Key points: Complementarity or unifying contradictions are concepts that allow for humility and pluralism in clinical and scientific medicine.

No MeSH data available.


The relation between increasing complexity (amount of data) and the total number of errors in a scientific model. The figure illustrates a U-shaped relation resulting from diminishing bias (increased validity of the scientific model) and an increasing variation (reduced reliability of the model). A trade-off with an optimal interval is defined in the figure.[8]
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Figure 0001: The relation between increasing complexity (amount of data) and the total number of errors in a scientific model. The figure illustrates a U-shaped relation resulting from diminishing bias (increased validity of the scientific model) and an increasing variation (reduced reliability of the model). A trade-off with an optimal interval is defined in the figure.[8]

Mentions: These conflicts are also fuelled by the introduction of personalised medicine or P4 medicine, which stands for predictive, personalised, preventive, and participatory. This initiative claims that it is a unifying effort answering the challenges of the digital revolution around managing Big Data. Such data will create deep insights into disease mechanisms and create metrics for assessing wellness, according to the proponents of the theory.[7] The assumption behind P4 theory is that voluminous data from sensors and personal analytic devices, and information from a multitude of other sources, will yield enough data to outweigh the effects from random variation and will safeguard reasonable protection against type I errors (erroneous confirmation) and type II errors (erroneous rejection). We acknowledge this intention as important in science. However, warnings are raised that beyond a certain limit the noise from random variation and the risk of overemphasising irrelevant associations will increase. Figure 1 illustrates this. The optimal interval is a middle ground, as the figure illustrates.[8]


Why several truths can be true
The relation between increasing complexity (amount of data) and the total number of errors in a scientific model. The figure illustrates a U-shaped relation resulting from diminishing bias (increased validity of the scientific model) and an increasing variation (reduced reliability of the model). A trade-off with an optimal interval is defined in the figure.[8]
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036018&req=5

Figure 0001: The relation between increasing complexity (amount of data) and the total number of errors in a scientific model. The figure illustrates a U-shaped relation resulting from diminishing bias (increased validity of the scientific model) and an increasing variation (reduced reliability of the model). A trade-off with an optimal interval is defined in the figure.[8]
Mentions: These conflicts are also fuelled by the introduction of personalised medicine or P4 medicine, which stands for predictive, personalised, preventive, and participatory. This initiative claims that it is a unifying effort answering the challenges of the digital revolution around managing Big Data. Such data will create deep insights into disease mechanisms and create metrics for assessing wellness, according to the proponents of the theory.[7] The assumption behind P4 theory is that voluminous data from sensors and personal analytic devices, and information from a multitude of other sources, will yield enough data to outweigh the effects from random variation and will safeguard reasonable protection against type I errors (erroneous confirmation) and type II errors (erroneous rejection). We acknowledge this intention as important in science. However, warnings are raised that beyond a certain limit the noise from random variation and the risk of overemphasising irrelevant associations will increase. Figure 1 illustrates this. The optimal interval is a middle ground, as the figure illustrates.[8]

View Article: PubMed Central - PubMed

ABSTRACT

In this paper, we offer a perspective on complementarity, acknowledging that it is not possible for human perception and cognition to grasp reality with unambiguous concepts or theories. Therefore, multiple concepts and perspectives are valid when they are not exaggerated beyond reasonable limits and do not claim exclusive validity. We recommend a humble stance enabling respectful dialogue between different perspectives in medical science and practice.

Key points: No single perspective in clinical or scientific medicine can exhaustively explain medical phenomena.

Key points: Scientific attitude is characterised by a willingness to look for objections against what we prefer as truths.

Key points: Complementarity or unifying contradictions are concepts that allow for humility and pluralism in clinical and scientific medicine.

Key points: Complementarity or unifying contradictions are concepts that allow for humility and pluralism in clinical and scientific medicine.

No MeSH data available.