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Record W4320022997 · doi:10.15173/sciential.v1i9.3198

Dexamethasone's Connection to COVID-19

2022· article· en· W4320022997 on OpenAlex
Bianca Mammarella, Sarah Damiani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueSciential - McMaster Undergraduate Science Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsYork UniversityMcMaster University
Fundersnot available
KeywordsDexamethasoneCoronavirus disease 2019 (COVID-19)MedicineCuriosityIntensive care medicinePandemicCase fatality rateInternal medicinePsychologyDisease

Abstract

fetched live from OpenAlex

Dexamethasone is known for its use as an anti-inflammatory and immunosuppressant medication. This medication has been present for many years, and its benefits have been observed in the treatment of various conditions. With the rise of COVID-19 cases on an international scale, healthcare professionals globally searched for a therapeutic medication, either existing or under development that could help those who were ill with the virus. The Recovery Trial aims to find a pharmacotherapeutic medication that would assist in treating hospitalized individuals who were diagnosed with COVID-19. In this trial, Dexamethasone’s ability to reduce hospitalization durations, and patient fatality was observed. These results increased curiosity about Dexamethasone's potential in the fight against COVID-19. As we work towards a standardized treatment plan for COVID-19, investigate Dexame- thasone’s mechanisms of action, and how it impacts different populations; together, these findings may help to determine this medication’s effectiveness as a COVID-19 treatment option.

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.

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)0.011
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.755
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0050.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.075
GPT teacher head0.432
Teacher spread0.357 · 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