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Record W3216512889 · doi:10.4103/2468-8827.330647

Biomedical advances in the treatment of COVID-19

2021· article· en· W3216512889 on OpenAlexaffabout
R. Iyer, V. Venkataramanan, Grant N. Pierce, Nikita Thakkar, Valle Natarajan, Arun Chockalingam

Bibliographic record

VenueInternational Journal of Noncommunicable Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of TorontoUniversity of ManitobaSt. Boniface HospitalAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSummitWhite paperCoronavirus disease 2019 (COVID-19)PandemicPolitical scienceIncentive2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public relationsEconomic growthEngineering ethicsMedicineGeographyEngineeringVirologyEconomicsLaw

Abstract

fetched live from OpenAlex

This white paper summarizes the key outcomes, topics, and recommendations from the Canada-India Healthcare Summit 2021 Conference, Biotechnology Session, held on May 20–21, 2021. In particular, the authors have focused their attention on topics ranging from research and development into the etiology and treatment of COVID-19 to novel approaches, such as ultraviolet-C disinfection and cell and gene therapy. The paper also deals with important topics around the effects of food distribution and nutrition on COVID-19 and vice versa, as well as key considerations around research and development, innovation, policy, grants, and incentives, and finally, summarizes the ways in which Canada and India, being close allies, have already begun to partner to fight the pandemic (as well as future strategies to continue this excellent progress). We also include key points raised during the summit and summarize them as part of this white paper.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

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 science0.0010.000
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 head0.054
GPT teacher head0.437
Teacher spread0.383 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2021
Admission routes2
Has abstractyes

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