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Record W3016032432 · doi:10.1186/s12979-020-00180-2

Can an effective SARS-CoV-2 vaccine be developed for the older population?

2020· editorial· en· W3016032432 on OpenAlexaff
Graham Pawelec, Nan‐ping Weng

Bibliographic record

VenueImmunity & Ageing · 2020
Typeeditorial
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Medicine2019-20 coronavirus outbreakVirologySars virusPandemicPopulationEnvironmental healthInfectious disease (medical specialty)Internal medicineDisease

Abstract

fetched live from OpenAlex

The emergence of SARS-CoV-2 and its inordinately rapid spread is posing severe challenges to the wellbeing of millions of people worldwide, health care systems and the global economy. While many younger people experience no or mild symptoms on infection, older adults are highly susceptible to life-threatening respiratory and systemic conditions which demand a full understanding and leveraging of knowledge of the differences between immunity in young and old people. Consequently, we welcome papers addressing any issues relevant to immunity and ageing in the context of SARS-CoV-2, and will endeavour to fast-track peer-review. We aim to provide a platform exclusively for discussions of individual and age differences in susceptibility and immune responses to COVID caused by SARS-CoV-2 infection and how to prevent or reduce severity of disease in older adults.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0060.006
Open science0.0020.001
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.0160.014

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 head0.049
GPT teacher head0.389
Teacher spread0.340 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations58
Published2020
Admission routes1
Has abstractyes

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