Main outcome measure: The development of a clinical prediction rule for GDM.

We also identified clinical prediction rules and validated those using existing data sets.

Objective: Ideally, clinical prediction models are generalizable to other patient groups.

Unfortunately, it has not been any clinical prediction mark sufficiently sensitive to GC yet.

Design: We used data from a prospective cohort study to develop the clinical prediction rule.

Therefore there is a need for the development of clinical prediction rules (CPRs).

Objective: To define a clinical prediction rule for underimmunization in children of low socioeconomic status.

We developed and internally validated their clinical prediction models.

In fact, multiscale genomic features will provide more information and may be helpful for clinical prediction.

We sought to derive and validate a clinical prediction rule to risk-stratify patients for postoperative AF.

Each case will be discussed relative to known clinical prediction rules (CPRs) and published evidence.

These steps include validity assessment, updating (if necessary), and impact assessment of clinical prediction rules.

Conclusion: A clinical prediction rule was developed that accurately identified patients at low-risk or high-risk for MBT.

While clinical prediction tools exist, they do not incorporate the newest Infectious Diseases Society of America guidelines.

Our objective was to identify diagnostic criteria and to develop a clinical prediction rule for this disease.

One solution could be a clinical prediction model to support health professionals estimate the probability of an asthma diagnosis.