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Coding long COVID: characterizing a new disease through an ICD-10 lens
Hannah Davis, Rachel Wong, Emily Pfaff, Elaine Hill, Andrew T. Girvin, Johanna Loomba, Tellen D. Bennett, John M. Baratta, Charisse Madlock‐Brown, Kristin Kostka, Melissa Haendel, Elizabeth Kelly, Christopher G. Chute, Abhishek Bhatia, Richard A. Moffitt, Julie A. McMurry
Bibliographic record
VenueUNC Libraries · 2025
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 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, IrvineOregon 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 HospitalVanderbilt University Medical CenterTranslational Research Institute, University of Arkansas for Medical SciencesNorthShore University HealthSystemRutgers, The State University of New JerseyUniversity of RochesterAurora Health CareInstitute of Clinical and Translational SciencesBill and Melinda Gates FoundationUniversity of WashingtonUniversity of California, Los AngelesInstitute for Translational Medicine and TherapeuticsGeorgia Clinical and Translational Science AllianceVirginia Commonwealth UniversityBrown UniversityRush UniversityCincinnati Children's Hospital Medical CenterYale UniversityUniversity of Wisconsin-MadisonTulane UniversityPennsylvania State UniversityVanderbilt Institute for Clinical and Translational ResearchInstitute for Clinical and Translational Research, University of Wisconsin, MadisonUniversity of California, San DiegoJohns Hopkins UniversityFrontiers Clinical and Translational Science Institute, University of KansasUniversity of Texas Health Science Center at San AntonioLoyola University ChicagoWashington 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 ChicagoOhio State UniversityPenn State Clinical and Translational Science InstituteYork UniversityUniversity of MiamiCenter for Clinical and Translational ResearchEmory UniversityCarilion Clinic
KeywordsMedical diagnosisPopulationPandemicCoding (social sciences)DiseaseLeverage (statistics)Context (archaeology)
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score1.000
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
Opus teacher head0.028
GPT teacher head0.305
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
Study designObservational
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations0
Published2025
Admission routes1
Has abstractno