Diagnostic prediction models can support the diagnostic process, both for experienced physicians and for physicians with little experience. More attention should be paid to the incorporation of diagnostic prediction models in the electronic patient record, so that a more accurate probability estimate can be made without simplification to rounded sumscores. A uniform cut-off of sum scores with associated categorization is also undesirable, because it does not take the context of the individual patient sufficiently into account. In the case of a very strong gut feeling, the outcome of a diagnostic prediction model rule alone cannot be sufficient for further policy. Diagnostic prediction models 'only' generate individual objectively estimated ...
textabstractClinical prediction models provide risk estimates for the presence of disease (diagnosis...
A disease which is left untreated for a longer period is more likely to cause negative consequents f...
International audienceThe determination of the clinical pretest probability using clinical predictio...
Diagnostic prediction models can support the diagnostic process, both for experienced physicians and...
Clinical prediction models play an increasingly important role in contemporary clinical care, by inf...
A clinical prediction model can assist doctors in arriving at the most likely diagnosis or estimatin...
Evaluation of diagnostic tests may seem a straightforward practice at first sight, but unfortunately...
- A prediction rule is a statistical model that can be used to predict the presence or absence of a ...
IMPORTANCE Prognostication is an important aspect of clinical decision-making, but it is often chall...
Prediction modelling, both diagnostic and prognostic, has become a major topic in clinical research ...
Clinical prediction rules (CPRs) have become more prevalent in the published literature in recent ye...
Background: Diagnostic clinical prediction rules (CPRs) are developed to improve diagnosis or decrea...
The past decades there has been a clear shift from implicit to explicit diagnosis and prognosis. Thi...
A probability model expresses the relation between the presence of clinical findings (input or indep...
Diagnostic clinical prediction rules (CPRs) are developed to improve diagnosis or decrease diagnosti...
textabstractClinical prediction models provide risk estimates for the presence of disease (diagnosis...
A disease which is left untreated for a longer period is more likely to cause negative consequents f...
International audienceThe determination of the clinical pretest probability using clinical predictio...
Diagnostic prediction models can support the diagnostic process, both for experienced physicians and...
Clinical prediction models play an increasingly important role in contemporary clinical care, by inf...
A clinical prediction model can assist doctors in arriving at the most likely diagnosis or estimatin...
Evaluation of diagnostic tests may seem a straightforward practice at first sight, but unfortunately...
- A prediction rule is a statistical model that can be used to predict the presence or absence of a ...
IMPORTANCE Prognostication is an important aspect of clinical decision-making, but it is often chall...
Prediction modelling, both diagnostic and prognostic, has become a major topic in clinical research ...
Clinical prediction rules (CPRs) have become more prevalent in the published literature in recent ye...
Background: Diagnostic clinical prediction rules (CPRs) are developed to improve diagnosis or decrea...
The past decades there has been a clear shift from implicit to explicit diagnosis and prognosis. Thi...
A probability model expresses the relation between the presence of clinical findings (input or indep...
Diagnostic clinical prediction rules (CPRs) are developed to improve diagnosis or decrease diagnosti...
textabstractClinical prediction models provide risk estimates for the presence of disease (diagnosis...
A disease which is left untreated for a longer period is more likely to cause negative consequents f...
International audienceThe determination of the clinical pretest probability using clinical predictio...