Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis
We found evidence of substantial disagreement between clinicians on diagnostic criteria for PCC. The variation in diagnostic rates across clinicians points to the possibilities of under- and over-diagnosis for patients.
Authors
- Emilia H. Koumans (ORCID: https://orcid.org/0000-0001-7184-2107)
- Sharon Saydah (ORCID: https://orcid.org/0000-0002-6350-5300)
- Nathaniel Hendrix (ORCID: https://orcid.org/0000-0001-8154-0276)
- Oscar Rincón‐Guevara (ORCID: https://orcid.org/0000-0003-1739-5769)
- Grace Walter (ORCID: https://orcid.org/0000-0002-4481-823X)
- Madeline Taskier (ORCID: https://orcid.org/0009-0001-7175-7524)
- Robert L. Phillips (ORCID: https://orcid.org/0000-0001-7882-1560)
- David H. Rehkopf (ORCID: https://orcid.org/0000-0002-7597-6513)
- Ilia Rochlin
- Rishi V Parikh (ORCID: https://orcid.org/0000-0002-1115-844X)
Institutions
- American Academy of Family Physicians (US)
- Centers for Disease Control and Prevention (US)
- National Center for Immunization and Respiratory Diseases (US)
- American Health Care Association (US)
- Stanford Health Care (US)
- Stanford Medicine (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-05-16
- DOI
- https://doi.org/10.1371/journal.pone.0324017
- Citations
- 1
- Primary Topic
- Chronic Disease Management Strategies
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Centers for Disease Control and Prevention