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Record W4377041779 · doi:10.3390/medicines10050032

Secreted Protein Acidic and Rich in Cysteine (SPARC) to Manage Coronavirus Disease-2019 (COVID-19) Pandemic and the Post-COVID-19 Health Crisis

2023· article· en· W4377041779 on OpenAlex

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.

Bibliographic record

VenueMedicines · 2023
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakCoronavirusDiseasePublic healthMedicineVirologyOutbreakInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

Coronavirus disease-2019 (COVID-19) has had and will have impacts on public health and health system expenses. Indeed, not only it has led to high numbers of confirmed COVID-19 cases and hospitalizations, but its consequences will remain even after the end of the COVID-19 crisis. Therefore, therapeutic options are required to both tackle the COVID-19 crisis and manage its consequences during the post COVID-19 era. Secreted protein acidic and rich in cysteine (SPARC) is a biomolecule that is associated with various properties and functions that situate it as a candidate which may be used to prevent, treat and manage COVID-19 as well as the post-COVID-19-era health problems. This paper highlights how SPARC could be of such therapeutic use.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.039
GPT teacher head0.324
Teacher spread0.285 · 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