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Record W3182239521 · doi:10.1136/bmj.n1687

Should we delay covid-19 vaccination in children?

2021· article· en· W3182239521 on OpenAlexaff
Dominic Wilkinson, Ilora Finlay, Andrew J. Pollard, Lisa Forsberg, Anthony Skelton

Bibliographic record

VenueBMJ · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsWestern University
FundersUniversity of OxfordNational Institute for Health and Care ResearchArts and Humanities Research CouncilUK Research and InnovationWellcome Trust
KeywordsDisadvantagedVaccinationCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicinePediatricsVirologyFamily medicinePolitical scienceLawInternal medicineOutbreak

Abstract

fetched live from OpenAlex

The net benefit of vaccinating children is unclear, and vulnerable people worldwide should be prioritised instead, say Dominic Wilkinson, Ilora Finlay, and Andrew J Pollard. But Lisa Forsberg and Anthony Skelton argue that covid-19 vaccines have been approved for some children and that children should not be disadvantaged because of policy choices that impede global vaccination

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)0.008
metaresearch head score (Gemma)0.075
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.030
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.075
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.008
Open science0.0020.003
Research integrity0.0140.017
Insufficient payload (model declined to judge)0.0210.004

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 head0.382
Teacher spread0.328 · 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
GenreCommentary

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

Citations24
Published2021
Admission routes1
Has abstractyes

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