Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
VenueBMC Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
FundersYale Center for Clinical Investigation, Yale School of MedicineColorado Clinical and Translational Sciences InstituteNational Center for Advancing Translational SciencesNational Institute of Environmental Health SciencesNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteClinical and Translational Science Center, University of New MexicoClinical and Translational Science Institute, Boston UniversityCenter for Clinical and Translational Sciences, University of Texas Health Science Center at HoustonInstitute for Integration of Medicine and ScienceCenter for Clinical and Translational Science, Mayo ClinicUniversity of Colorado DenverLeonard M. Miller School of MedicineUniversity of California, IrvineUniversity of North Carolina at Chapel HillOregon Clinical and Translational Research InstituteUniversity of California, DavisWeill Cornell Medical CollegeUniversity of Illinois at Urbana-ChampaignNational Institutes of HealthInstitute for Clinical and Translational Science, University of California, IrvineOchsner HealthUniversity of California, San FranciscoLouisiana Clinical and Translational Science CenterTufts Medical CenterInstitute of Translational Health SciencesChildren's National HospitalUniversity of Arkansas for Medical SciencesVanderbilt University Medical CenterTranslational Research Institute, University of Arkansas for Medical SciencesNorthShore University HealthSystemRutgers, The State University of New JerseyUniversity at BuffaloUniversity of RochesterAurora Health CareInstitute of Clinical and Translational SciencesInstitute for Translational Medicine and TherapeuticsUniversity of California, San DiegoJohns Hopkins UniversityInstitute for Clinical and Translational Research, University of Wisconsin, MadisonPennsylvania State UniversityVanderbilt Institute for Clinical and Translational ResearchUniversity of California, Los AngelesBill and Melinda Gates FoundationUniversity of WashingtonFrontiers Clinical and Translational Science Institute, University of KansasUniversity of Texas Health Science Center at San AntonioLoyola University ChicagoUniversity of Texas Health Science Center at HoustonWashington University in St. LouisUniversity of MichiganHarvard CatalystUniversity of MinnesotaChildren's Hospital of PhiladelphiaMichigan Institute for Clinical and Health ResearchUniversity of UtahUniversity of PennsylvaniaGeorge Washington UniversityVanderbilt UniversityAccelerated Innovation Research Initiative Turning Top Science and Ideas into High-Impact ValuesUniversity of ChicagoGeorgia Clinical and Translational Science AllianceVirginia Commonwealth UniversityTulane UniversityBrown UniversityRush UniversityCincinnati Children's Hospital Medical CenterYale UniversityUniversity of Wisconsin-MadisonOhio State UniversityPenn State Clinical and Translational Science InstituteChildren's Hospital ColoradoYork UniversityUniversity of MiamiCenter for Clinical and Translational ResearchEmory UniversityCarilion Clinic
KeywordsMedicineMedical diagnosisPopulationPandemicContext (archaeology)DiseaseCoronavirus disease 2019 (COVID-19)Diagnosis codeCoding (social sciences)Intensive care medicineInfectious disease (medical specialty)PathologyEnvironmental healthStatistics
Abstract
fetched live from OpenAlexBACKGROUND: Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. RESULTS: We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. CONCLUSIONS: This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
