Table 2.

Predicting health and disease.

A. Why it is difficult to predict the (clinical) future for the individual: the boundary between health and disease is fuzzy.
→ Predictability relies on:
• formulation of relevant questions
• using appropriate methodology
• having more evidence
• strong algorithms
• interpretative model
• generalizability of population averages to individuals is problematic:
→ Interpretative models are:
• subjective interpretation of the researcher
• influenced by the dominant scientific culture
B. Limitations in the use of statistical models in medicine.
• A model is a subjective interpretation of the researcher.
• A model lacks a complete -structural-systemic- understanding.
• The predictability of a model relies on algorithms.
• In statistics, any correlation found does not imply causation.
• Reproducibility is a standard, mainly, for science.
• Future exposures may not be predictable precisely.
• The generalization of results is impossible.
• The verification of the prevention effectiveness is a complex issue.
C. Critical approach to the guidelines system.
Multiple instances, difficult to meet at the same time, built a (better) system of (public) health care
• rationalizing the medical intervention
• reduce costs
• ensure legal protection of physicians
• preserve professional autonomy
• fall within the public/private funding of research
• ensure an appropriate statistical-mathematical standard
• check the prevention effectiveness
D. Different approaches to the patient if viewed as an individual or as an average.
Individual patient	Average patient
Requires an empathic relationship	Relationship should be limited
Benefit from the consultation of the guidelines	Use of guidelines mandatory
Takes more time	Takes less time
Decision making assumes a high level of responsibility	Responsibilities are shared