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Record W3107228882 · doi:10.1093/emph/eoaa046

Conflicts over calcium and the treatment of COVID-19

2020· review· en· W3107228882 on OpenAlexaff
Bernard J. Crespi, Joe Alcock

Bibliographic record

VenueEvolution Medicine and Public Health · 2020
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAmlodipineNifedipineCoronavirus disease 2019 (COVID-19)CalciumCalcium metabolismCoronavirusVirusCalcium channelHost (biology)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakVirologyBiologyMedicineDiseaseImmunologyInternal medicineInfectious disease (medical specialty)GeneticsBlood pressure

Abstract

fetched live from OpenAlex

Several recent studies have provided evidence that use of calcium channel blockers (CCBs), especially amlodipine and nifedipine, can reduce mortality from coronavirus disease 2019 (COVID-19). Moreover, hypocalcemia (a reduced level of serum ionized calcium) has been shown to be strongly positively associated with COVID-19 severity. Both effectiveness of CCBs as antiviral therapy, and positive associations of hypocalcemia with mortality, have been demonstrated for many other viruses as well. We evaluate these findings in the contexts of virus-host evolutionary conflicts over calcium metabolism, and hypocalcemia as either pathology, viral manipulation or host defence against pathogens. Considerable evidence supports the hypothesis that hypocalcemia represents a host defence. Indeed, hypocalcemia may exert antiviral effects in a similar manner as do CCBs, through interference with calcium metabolism in virus-infected cells. Prospective clinical studies that address the efficacy of CCBs and hypocalcemia should provide novel insights into the pathogenicity and treatment of COVID-19 and other viruses.

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)0.003
metaresearch head score (Gemma)0.035
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
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 head0.355
GPT teacher head0.560
Teacher spread0.205 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations62
Published2020
Admission routes1
Has abstractyes

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