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Record W3213934596 · doi:10.5281/zenodo.5033246

Early Treatment of the Inflammatory Stage of COVID-19 and its rationale

2021· article· en· W3213934596 on OpenAlexaff
Elizabeth Bastian, Niel A. Karrow, Ondrej Halgas, Kanji Nakatsu

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsQueen's UniversityUniversity of GuelphUniversity of TorontoBC Cancer Agency
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Stage (stratigraphy)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineIntensive care medicineVirologyInternal medicineDiseaseBiologyInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

Abstract COVID-19 is typically an acute infection caused by SARS-CoV-2 virus that lasts 2-3 weeks. However, in some instances the disease may worsen and lead to Acute Respiratory Distress Syndrome (ARDS) and multisystem damage that can result in long term disability or even death if not treated early and effectively. This article focuses on the early inflammatory phase of COVID-19, which typically starts about a week after the onset of first symptoms associated with the initial viral stage. A readily accessible treatment option comprising well-known glucocorticoids, antihistamines, leukotriene antagonists and anticoagulants is included, and its rationale is given. This treatment option is especially relevant for general practitioners and emergency room clinicians and can be initiated prior to the need for hospitalization and in most cases may also prevent it.

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)0.001
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: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.036
GPT teacher head0.285
Teacher spread0.248 · 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
GenreCommentary

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
Published2021
Admission routes1
Has abstractyes

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