{"doi":"10.1101/2025.03.26.25324631","title":"Real-World Type 2 Diabetes Second-Line Treatment Allocation Among Patients","abstract":"<h4>Objective</h4> This study aimed to evaluate the impact of socioeconomic disparities on the allocation of second-line treatments among patients with type 2 diabetes (T2D). <h4>Materials and Methods</h4> We conducted an observational study using real-world data from over 9 million patients across five University of California Health centers. The study included patients who initiated a second-line T2D medication after metformin, with hemoglobin A1c (HbA1c) measurements within ±7 days of treatment initiation from 2012 through September 2024. Multinomial regression models assessed the association between socioeconomic status and second-line treatment choices. Additionally, we used the GPT-4 large language model with a zero-shot learning approach to analyze 270 clinical notes from 105 UCSF patients. GPT-4 identified adverse social determinants of health (SDOH) across six domains: transportation, housing, relationships, patients with children, support, and employment. <h4>Results</h4> Among 15,090 patients (56.7% male, 43.3% female; mean age 59.3 years; mean HbA1c 8.91%), second-line treatments included sulfonylureas (SUs; n = 6,732), DPP4 inhibitors (n = 2,918), GLP-1 receptor agonists (n = 2,736), and SGLT2 inhibitors (n = 2,704). Patients from lower socioeconomic neighborhoods were more likely to receive SUs over other medications: DPP4i (OR = 0.96, [95% CI, 0.95-0.98]), GLP-1RA (OR = 0.94, [95% CI, 0.92-0.96]), SGLT2i (OR = 0.95, [95% CI, 0.93-0.97]). In UCSF clinical notes, we identified adverse SDOH including housing (n=8), transportation (n=1), relationships (n=22), employment (n=12), support (n=1), and patients with children (n=25). <h4>Conclusions</h4> Socioeconomic factors influence second-line T2D treatment choices. Addressing these disparities is essential to ensuring equitable access to advanced T2D therapies.","journal":null,"year":2025,"id":5707,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":0.0,"corpus_rank":765,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.529,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-03-27","fair_score":31.0417,"fair_percentile":12.620932277924362,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":32096,"name":"Rohit Vashisht","orcid":"0000-0002-2938-7259","position":1,"is_corresponding":false},{"id":32097,"name":"Ayan Patel","orcid":null,"position":3,"is_corresponding":false},{"id":29204,"name":"Suneil K. Koliwad","orcid":"0000-0002-7367-1054","position":4,"is_corresponding":false},{"id":51,"name":"Atul Janardhan Butte","orcid":"0000-0002-7433-2740","position":5,"is_corresponding":false},{"id":55264,"name":"Kendra K. Radtke","orcid":"0000-0002-0578-6554","position":6,"is_corresponding":false},{"id":32120,"name":"Ayan R. Patel","orcid":"0000-0003-1984-1400","position":7,"is_corresponding":false},{"id":42603,"name":"Jaysón Davidson","orcid":"0000-0001-9066-9872","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":"40196266","pmcid":"PMC11974982","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":"green","license":"cc-by-nd","views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":52.5,"fair_a":30.0,"fair_i":25.0,"fair_r":16.6667,"fair_zscore":-1.2817,"fair_rationale":{"fair_score":31.04,"has_llm":true,"dimensions":{"F":{"name":"Findable","score":52.5,"criteria":[{"key":"f_has_doi","label":"Has a persistent DOI","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"DOI present","rationale":null},{"key":"f_repository_presence","label":"Indexed in repositories / literature DBs","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"datacite=0, pmcid=True, pmid=True","rationale":null},{"key":"f_persistent_ids","label":"Resolvable scholarly identifiers (OpenAlex)","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"no OpenAlex id","rationale":null},{"key":"f_metadata_richness","label":"Rich, machine-readable metadata","kind":"llm","weight":1.0,"fraction":0.25,"signal":null,"rationale":"The paper includes a DOI and license but lacks any machine-readable metadata such as structured data markup or formal metadata descriptions."}]},"A":{"name":"Accessible","score":30.0,"criteria":[{"key":"a_open_access","label":"Open Access / files deposited","kind":"deterministic","weight":1.5,"fraction":1.0,"signal":"Open Access","rationale":null},{"key":"a_retrievable","label":"Free full text retrievable","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"0 OA location(s)","rationale":null},{"key":"a_access_protocol","label":"Clear data/code access protocol","kind":"llm","weight":1.0,"fraction":0.0,"signal":null,"rationale":"No data or code access protocol is provided; the data source (UC Health) is not publicly accessible and no repository or request procedure is mentioned."}]},"I":{"name":"Interoperable","score":25.0,"criteria":[{"key":"i_linked_data","label":"Linked datasets / DataCite relations","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"linked_datasets=0, datacite=0","rationale":null},{"key":"i_standard_ids","label":"References data via standard accessions","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"accessions=0, trials=0","rationale":null},{"key":"i_standards","label":"Standard formats, vocabularies & identifiers","kind":"llm","weight":1.0,"fraction":0.5,"signal":null,"rationale":"The study uses the OMOP common data model and standard clinical vocabularies (ICD-10, ADI), but the paper itself does not provide machine-readable standard formats or identifiers for its content."}]},"R":{"name":"Reusable","score":16.67,"criteria":[{"key":"r_license","label":"Clear, open reuse license","kind":"deterministic","weight":1.5,"fraction":0.0,"signal":"no license","rationale":null},{"key":"r_downloads","label":"Demonstrated reuse (downloads)","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"downloads=0","rationale":null},{"key":"r_version","label":"Versioned / maintained","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"no version chain","rationale":null},{"key":"r_dataset","label":"Classified as a data resource","kind":"deterministic","weight":0.5,"fraction":1.0,"signal":"is_dataset","rationale":null},{"key":"r_reusability","label":"Data-availability statement, license & reproducibility","kind":"llm","weight":2.0,"fraction":0.167,"signal":null,"rationale":"A CC BY-ND license is present, but there is no data availability statement, no code repository, and insufficient detail to reproduce the analysis without access to the proprietary data."}]}},"suggestions":["Add a data availability statement specifying how to request access to the UC Health data or provide a de-identified dataset.","Deposit analysis code (e.g., R scripts) in a public repository like GitHub or Zenodo with a persistent identifier.","Include machine-readable metadata (e.g., schema.org markup) in the paper's HTML version to improve findability.","Provide a clear protocol for accessing the underlying data, such as a contact for data use agreements or a link to a data portal.","Document the GPT-4 prompts and validation steps in a supplementary file to enhance reproducibility."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v2","fulltext_source":"epmc_xml"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v2","fair_fulltext_source":"epmc_xml","fair_has_llm":true,"fair_computed_at":"2026-06-18T06:51:35.821297Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}