MétaCan
Menu
Back to cohort
Record W3163944937 · doi:10.3389/fped.2021.669453

Renal Infarcts—A Perplexing Case in the Middle of the COVID-19 Pandemic

2021· review· en· W3163944937 on OpenAlexaff
Brett Plouffe, Tamara Van Hooren, Michelle Barton, Nancy Nashid, Erkan Demirkaya, Kambiz Norozi, Irina Rachinsky, Johan Delport, Michael J. Knauer, Soumitra Tole, Guido Filler

Bibliographic record

VenueFrontiers in Pediatrics · 2021
Typereview
Languageen
FieldMedicine
TopicKawasaki Disease and Coronary Complications
Canadian institutionsChildren’s Health Research InstituteLawson Health Research InstituteWestern University
FundersUniversity of North Carolina at Chapel Hill
KeywordsMedicinePandemicCoronavirus disease 2019 (COVID-19)PediatricsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)InfarctionComplicationPresentation (obstetrics)Intensive care medicineInternal medicineMyocardial infarctionSurgeryInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Renal infarction is a rare finding in children. Associations between SARS-CoV-2 infections and thromboembolic events including renal infarcts have been described in adults. Although a similar association in children has not yet been described with this pandemic, the pediatric literature is still evolving with the recognition of new manifestations including the post-infectious Multisystem Inflammatory Syndrome in Children (MIS-C). We report the rare event of multiple renal infarcts in a 6-year-old boy manifesting several features of MIS-C 9 weeks following a self-limiting febrile illness characteristic of COVID-19. An underlying Factor V Leiden mutation was identified in this child but felt to be insufficient on its own to explain his clinical presentation. As SARS-CoV-2 testing was delayed, the failure to identify viral RNA or antibodies may not exclude the virus' potential role in precipitating the infarct in this host. Given that renal infarcts have been described in adult patients with COVID-19, reporting this perplexing case where SARS-CoV-2 may have played a role, may help identify this potential complication.

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 distilled prediction

Teacher imitation

Not 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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.800
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.143
GPT teacher head0.368
Teacher spread0.224 · 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 teacher head, 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

Citations13
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in PediatricsSame topicKawasaki Disease and Coronary ComplicationsFrench-language works237,207