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Record W4407946790 · doi:10.1097/cnq.0000000000000544

The Effect of Prior Use of Statins on the Severity of COVID-19 Disease

2025· article· en· W4407946790 on OpenAlexaff
Hadi Hasani, Farzaneh Hamidi, Fatemeh Ahmadi-Forg, Fatemeh Tofighi Khelejan

Bibliographic record

VenueCritical Care Nursing Quarterly · 2025
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineOxygen saturationRetrospective cohort studyCoronavirus disease 2019 (COVID-19)PillStatinMechanical ventilationVentilation (architecture)Internal medicineSingle CenterEmergency medicineDiseaseInfectious disease (medical specialty)OxygenPharmacology

Abstract

fetched live from OpenAlex

It has been suggested that the use of statin pills beforehand could potentially influence the outcomes when individuals are hospitalized with COVID-19. In this study, we investigated how the prior use of statin medication could influence the COVID-19 severity parameters. In this retrospective cohort study, we categorized COVID-19 patients into 2 groups: statin users and non-users. Then, various data including age, gender, the patient's need for ventilation support, the lowest oxygen blood saturation level, the length of hospitalization, receiving remdesivir treatment, and their COVID-19 vaccination status were collected. Out of 168 patients, 62 had taken statin medication before being admitted. Using statins decreased the patient's need for ventilation support, length of hospitalization, ventilation duration, and oxygen saturation level (P < .001). Interaction effect analysis showed that receiving remdesivir statically affected the length of hospitalization, ventilation duration, and oxygen saturation level but did not significantly affect the association between statins and needing to ventilator. The use of statin pills before COVID-19 admission reduced the requirement for ventilator support.

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)0.139
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.139
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.480
Teacher spread0.425 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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