Update README.md

README.md

@@ -34,30 +34,6 @@ This model card describes the model associated with the manuscript "Uncertainty-
 
 # Uses
 
-## Direct Use
-This model is intended for research purposes only. Possible research areas and tasks include
-
-- Development and comparison of uncertainty quantification methods for pathologic images.
-- Probing and understanding the limitations of out-of-distribution detection for pathology classification models.
-- Applications in educational settings.
-- Research on pathology classification models for non-small cell lung cancer.
-
-Excluded uses are described below.
-
-### Misuse and Out-of-Scope Use
-This model should not be used in a clinical setting to generate predictions that will be used to inform patients, physicians, or any other health care members directly involved in their health care outside the context of an approved research protocol. Using the model in a clinical setting outside the context of an approved research protocol is a misuse of this model. This includes, but is not limited to:
-
-- Generating predictions of images from a patient's tumor and sharing those predictions with the patient
-- Generating predictions of images from a patient's tumor and sharing those predictions with the patient's physician, or other members of the patient's healthcare team
-- Influencing a patient's health care treatment in any way based on output from this model
-
-### Limitations
-
-- The model has not been validated to discriminate lung adenocarcinoma vs. squamous cell carcinoma in contexts where other tumor types are possible (such as lung small cell carcinoma, neuroendocrine tumors, metastatic deposits, etc.)
-
-### Bias
-This model was trained on The Cancer Genome Atlas (TCGA), which contains patient data from communities and cultures which may not reflect the general population. This datasets is comprised of images from multiple institutions, which may introduce a potential source of bias from site-specific batch effects ([Howard, 2021](https://www.nature.com/articles/s41467-021-24698-1)). The model was validated on data from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) and an institutional dataset from Mayo Clinic, the latter of which consists primarily of data from patients of white and western cultures.
-
 ## Examples
 For direct use, the model can be loaded using [Slideflow](https://github.com/jamesdolezal/slideflow) version 1.1 with the following syntax:
 
@@ -88,6 +64,30 @@ P = sf.Project('/path/to/slideflow/project')
 P.predict('/model/path')
 ```
 
+## Direct Use
+This model is intended for research purposes only. Possible research areas and tasks include
+
+- Development and comparison of uncertainty quantification methods for pathologic images.
+- Probing and understanding the limitations of out-of-distribution detection for pathology classification models.
+- Applications in educational settings.
+- Research on pathology classification models for non-small cell lung cancer.
+
+Excluded uses are described below.
+
+### Misuse and Out-of-Scope Use
+This model should not be used in a clinical setting to generate predictions that will be used to inform patients, physicians, or any other health care members directly involved in their health care outside the context of an approved research protocol. Using the model in a clinical setting outside the context of an approved research protocol is a misuse of this model. This includes, but is not limited to:
+
+- Generating predictions of images from a patient's tumor and sharing those predictions with the patient
+- Generating predictions of images from a patient's tumor and sharing those predictions with the patient's physician, or other members of the patient's healthcare team
+- Influencing a patient's health care treatment in any way based on output from this model
+
+### Limitations
+
+- The model has not been validated to discriminate lung adenocarcinoma vs. squamous cell carcinoma in contexts where other tumor types are possible (such as lung small cell carcinoma, neuroendocrine tumors, metastatic deposits, etc.)
+
+### Bias
+This model was trained on The Cancer Genome Atlas (TCGA), which contains patient data from communities and cultures which may not reflect the general population. This datasets is comprised of images from multiple institutions, which may introduce a potential source of bias from site-specific batch effects ([Howard, 2021](https://www.nature.com/articles/s41467-021-24698-1)). The model was validated on data from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) and an institutional dataset from Mayo Clinic, the latter of which consists primarily of data from patients of white and western cultures.
+
 ## Training
 
 **Training Data**