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Record W4413680771 · doi:10.1172/jci.insight.194314

The role of complement in long COVID pathogenesis

2025· review· en· W4413680771 on OpenAlexaff
Rafael Bayarri‐Olmos, William Bain, Akiko Iwasaki

Bibliographic record

VenueJCI Insight · 2025
Typereview
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsInstitute of Infection and Immunity
FundersYale UniversityU.S. Department of Veterans Affairs
KeywordsPathogenesisImmunologyCoronavirus disease 2019 (COVID-19)DiseaseComplement systemInflammationMedicineImmune systemImmune dysregulationInnate immune systemMalaisePathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Long COVID is a debilitating condition that can develop after a SARS-CoV-2 infection and is characterized by a wide range of chronic symptoms, including weakness, neurocognitive impairment, malaise, fatigue, and many others, that affect multiple organ systems. At least 10% of individuals with a previous infection may develop long COVID, which affects their ability to perform daily functions and work. Despite its severity and widespread impact, this multisystemic condition remains poorly understood. Recent studies suggest that dysregulation of the complement system, a key component of the innate immune response, may contribute to the pathogenesis of long COVID, particularly in connection with coagulation, inflammation, and vascular injury. In this Review, we examine the evidence linking complement system dysregulation to long COVID and explore its potential role in driving disease pathology.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.358
Teacher spread0.330 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2025
Admission routes1
Has abstractyes

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