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Record W4387345257 · doi:10.1186/s40794-023-00201-0

Strongyloides hyperinfection syndrome precipitated by immunosuppressive therapy for rheumatoid arthritis and COVID-19 pneumonia

2023· article· en· W4387345257 on OpenAlexaff
Hasan Hamze, Teresa Wai Chi Tai, D. James Harris

Bibliographic record

VenueTropical Diseases Travel Medicine and Vaccines · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineStrongyloides stercoralisStrongyloidiasisImmunosuppressionPneumoniaRheumatoid arthritisPandemicImmunologyDiseaseComplicationCoronavirus disease 2019 (COVID-19)Intensive care medicineInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has posed clinical and public health challenges worldwide. The use of corticosteroids has become an evidence-based practice to reduce the hyperinflammatory process involved in severe COVID-19 disease. However, this can result in the reactivation of parasitic infestations, even with a short course. We report the case of a 64-year-old Cuban born patient who passed away from S. stercoralis hyperinfection syndrome following treatment with dexamethasone for severe COVID-19 disease on a background of prolonged immunosuppression for rheumatoid arthritis. Clinicians should be aware of the risk of strongyloidiasis as a complication of the treatment for severe COVID-19 and other immunosuppressive therapies. We recommend empiric Strongyloides treatment for those who are from, or who have accumulated risk by travelling to endemic areas, and are being treated with corticosteroids for severe COVID-19 disease.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.315
Teacher spread0.292 · 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 designCase report
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

Citations4
Published2023
Admission routes1
Has abstractyes

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