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Record W3166903054 · doi:10.47326/ocsat.2021.02.30.1.0

Ivermectin to Prevent Disseminated Strongyloides Infection in Patients with COVID-19

2021· report· en· W3166903054 on OpenAlexaboutno aff
Elizabeth Leung, Mark McIntyre, Nisha Andany, William Ciccotelli, Christopher J Graham, Peter Jüni, Bradley J. Langford, Anne McCarthy, Caroline Nott, Wayne L. Gold, Menaka Pai, Samir Patel, Sumit Raybardhan, Nathan M. Stall, Andrew M. Morris

Bibliographic record

Venuenot available
Typereport
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthTable (database)MandateMedicinePandemicScientific evidenceCoronavirus disease 2019 (COVID-19)Family medicineExecutive orderHealth scienceMedical educationPublic relationsEnvironmental healthDiseasePolitical scienceInfectious disease (medical specialty)NursingPathologyComputer science

Abstract

fetched live from OpenAlex

Ivermectin, an antiparasitic agent, is currently not recommended for prophylaxis or treatment of COVID-19. Inappropriate use of ivermectin for COVID-19 may make it unavailable for patients who could benefit from its use (i.e., patients with serious parasitic infections) and reduce the already limited supply of ivermectin in Canada. However, patients with COVID-19 who receive immunomodulatory therapies (e.g., corticosteroids including dexamethasone, interleukin-6 inhibitors including tocilizumab) may be at risk of dissemination/hyperinfection syndrome from Strongyloides stercoralis, which can be fatal. We have developed a strategy to safely manage strongyloidiasis risk and infection in the setting of ivermectin shortage. Patients admitted to hospital with COVID-19 and at high epidemiologic risk for strongyloidiasis should be screened with serology. If a patient’s strongyloides serology is reactive or indeterminate, these patients should receive ivermectin to avoid the potential for parasitic dissemination/hyperinfection.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.339
Teacher spread0.320 · 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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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