Update select-publications.md

Author: BoyceLab

docs/select-publications.md

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 #  Select Publications 
 
-## Big data research is everyone’s research—Making epilepsy data science accessible to the global community: Report of the ILAE Big Data Commission  
+## 
+Big data research is everyone’s research—Making epilepsy data science accessible to the global community: Report of the ILAE Big Data Commission  
 **Josephson, C. B., Aronica, E., Beniczky, S., Boyce, D., Cavalleri, G., Denaxas, S., French, J., Jehi, L., Koh, H., Kwan, P., McDonald, C., Mitchell, J. W., Rampp, S., Sadleir, L., Sisodiya, S. M., Wang, I., Wiebe, S., Yasuda, C., Youngerman, B.** (2024). *Epileptic Disorders*.  
 
 A comprehensive review by the ILAE Big Data Commission aimed at demystifying how clinicians and researchers can engage with Big Data—including clinical, imaging, EEG, and common data models—in epilepsy research and care. It addresses practical, ethical, and equity-related challenges while showcasing the transformative potential of advanced data science methods.  
 
 📖 [Read the article](https://doi.org/10.1002/epd2.20288)  
 
-## Identification of patients with drug-resistant epilepsy in electronic medical record data using the Observational Medical Outcomes Partnership Common Data Model  
+## 
+Identification of patients with drug-resistant epilepsy in electronic medical record data using the Observational Medical Outcomes Partnership Common Data Model  
 **Castano, V. G.** *et al.* (2022). A study leveraging the OMOP Common Data Model to identify patients with drug-resistant epilepsy using EMR data. Demonstrates phenotype identification using standardized data structures.  
 📖 [PubMed link](https://pubmed.ncbi.nlm.nih.gov/36106377/)
 
-## Patient characteristics and antiseizure medication pathways in newly diagnosed epilepsy: Feasibility and pilot results using the common data model in a single-center electronic medical record database  
+## 
+Patient characteristics and antiseizure medication pathways in newly diagnosed epilepsy: Feasibility and pilot results using the common data model in a single-center electronic medical record database  
 **Spotnitz, M.** *et al.* (2022). A pilot demonstrating feasibility of characterizing adult epilepsy patients and their antiseizure medication treatment pathways using CDM in an EHR-derived dataset.  
 📖 [Read the article](https://pubmed.ncbi.nlm.nih.gov/35276502/)
 
-## Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model  
+## 
+Characterization of Anti-seizure Medication Treatment Pathways in Pediatric Epilepsy Using the Electronic Health Record-Based Common Data Model  
 **Kim, H.** *et al.* (2020). Analyzes pediatric epilepsy antiseizure medication treatment pathways using the OMOP CDM based on EHR data. Supports feasibility and applicability of CDM for pediatric epilepsy research.  
 📖 [Read the article](https://pubmed.ncbi.nlm.nih.gov/32477256/)
 
 ## Community Contributions
 
-### Submission: Drug-Resistant Epilepsy Phenotype to OHDSI Phenotype Library  
+### 
+Submission: Drug-Resistant Epilepsy Phenotype to OHDSI Phenotype Library  
 **Spotnitz, M.** (2022). Submission of the drug-resistant epilepsy phenotype—recently published in *Epilepsia* and showcased at OHDSI Symposium 2022—for peer review within the OHDSI Phenotype Library. This computable phenotype was developed using the OMOP Common Data Model (CDM) to systematically identify patients at Columbia University Irving Medical Center (CUIMC) meeting consensus criteria for drug-resistant epilepsy (failure of two or more appropriate medication regimens). The submission included a detailed clinical description, phenotype logic, and aims to contribute a reusable cohort definition to the open-science community.  
 📖 [OHDSI Forum Post](https://forums.ohdsi.org/t/phenotype-submission-drug-resistant-epilepsy/17569)