ArticleNPJ digital medicine2024
Finding Long-COVID: temporal topic modeling of electronic health records from the N3C and RECOVER programs.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Characterization and validation of EHR computable phenotypes for Long COVID using patient-reported symptoms: insights from the nationwide RECOVER program.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- SSRI prescription during acute COVID-19 and risk of Long COVID symptoms and conditions among patients with depression.medRxiv : the preprint server for health sciences · 2026Article
- Metformin and Severe Post-COVID-19 Outcomes Among Individuals with Diabetes Mellitus.medRxiv : the preprint server for health sciences · 2026Article
- GLP Medications and Severe Post-COVID-19 Outcomes Among Individuals with Type 2 Diabetes Mellitus.medRxiv : the preprint server for health sciences · 2026Article
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- IL-6 Receptor Antagonists and Severe Post-COVID-19 Outcomes: An Emulated Target Trial.medRxiv : the preprint server for health sciences · 2026Article
- A Systematic Review of Topic Modeling Techniques for Electronic Health Records.Healthcare (Basel, Switzerland) · 2026Review
- Decoding long COVID-associated cardiovascular dysfunction: Mechanisms, models, and new approach methodologies.Journal of molecular and cellular cardiology · 2025Review
- Label efficient phenotyping for Long COVID using electronic health records.NPJ digital medicine · 2025Article
- Research on New Methods of Topic Mining and Topic Prediction for Medical Preprints on Emerging Infectious Diseases.Cureus · 2025Article
Corrections and comments
- Update of
Authors and funding
21 authors.
Funding
Abstract
Post-Acute Sequelae of SARS-CoV-2 infection (PASC), also known as Long-COVID, encompasses a variety of complex and varied outcomes following COVID-19 infection that are still poorly understood. We clustered over 600 million condition diagnoses from 14 million patients available through the National COVID Cohort Collaborative (N3C), generating hundreds of highly detailed clinical phenotypes. Assessing patient clinical trajectories using these clusters allowed us to identify individual conditions and phenotypes strongly increased after acute infection. We found many conditions increased in COVID-19 patients compared to controls, and using a novel method to associate patients with clusters over time, we additionally found phenotypes specific to patient sex, age, wave of infection, and PASC diagnosis status. While many of these results reflect known PASC symptoms, the resolution provided by this unprecedented data scale suggests avenues for improved diagnostics and mechanistic understanding of this multifaceted disease.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.