The use of criterion tables is partially motivated by an additional shortcoming of Bayes theorem: its sequential nature of considering each finding one at a time. Sometimes, it is more convenient to consider many factors simultaneously while considering diseases. What if we imagined the diagnosis of a certain disease as an additive sum of select factors? That is, in the MI example, the patient receives a point for having positive chest pain, a point for having a history of a positive stress test, and so on. We could establish a threshold for a point total that gives a positive diagnosis of MI. Because some factors are more important than others, we could use a weighted sum, in which each factor is multiplied by an importance factor before adding. For example, the presence of chest pain may be worth three points, and a history of a positive stress test may be worth five points. This is how criterion tables work.
In the following table, we have given the modified wells criteria as an example. The modified wells criteria (derived from Clinical Prediction, 2017) are used to determine whether or not a patient may have a pulmonary embolism (PE): a blood clot in the lung that is life-threatening. Note that criterion tables not only provide point values for each relevant clinical finding but also give thresholds for interpreting the total score:
| Clinical finding | Score |
| Clinical symptoms of deep vein thrombosis (leg swelling, pain with palpation) | 3.0 |
| Alternative diagnosis is less likely than pulmonary embolism | 3.0 |
| Heart rate > 100 beats per minute | 1.5 |
| Immobilization for > 3 days or surgery in the previous 4 weeks | 1.5 |
| Previous diagnosis of deep vein thrombosis/pulmonary embolism | 1.5 |
| Hemoptysis | 1.0 |
| Patient has cancer | 1.0 |
| Risk stratification | |
| Low risk for PE | < 2.0 |
| Medium risk for PE | 2.0 - 6.0 |
| High risk for PE | > 6.0 |