MétaCan
Menu
← Back to cohort
Record W4405646192 · doi:10.1038/s41467-024-55350-3

Author Correction: Effectiveness of mRNA COVID-19 vaccine booster doses against Omicron severe outcomes

2024· erratum· en· W4405646192 on OpenAlexaff
Ramandip Grewal, Lena Nguyen, Sarah A. Buchan, Sarah E. Wilson, Sharifa Nasreen, Peter C. Austin, Kevin A. Brown, Deshayne B. Fell, Jonathan B. Gubbay, Kevin L. Schwartz, Mina Tadrous, Kumanan Wilson, Jeffrey C. Kwong

Bibliographic record

VenueNature Communications · 2024
Typeerratum
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsBruyèreOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of OttawaWomen's College HospitalUniversity Health NetworkUniversity of TorontoCentre for Disability Prevention and RehabilitationPublic Health Ontario
Fundersnot available
KeywordsBooster (rocketry)Coronavirus disease 2019 (COVID-19)VirologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakBooster doseMedicinePhysicsVirusInfectious disease (medical specialty)Internal medicineDiseaseOutbreak

Abstract

fetched live from OpenAlex

The original version of this Article contained errors in the Supplementary Information. The section titled “Supplementary Text: Determination of symptom status at the time of SARS-CoV-2 testing” listed some symptoms that were not included in the analysis. The list has been corrected in the updated Supplementary Information pdf.

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)0.005
metaresearch head score (Gemma)0.094
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: Other · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.094
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0700.029

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 head0.044
GPT teacher head0.406
Teacher spread0.362 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueNature Communications→Same topicSARS-CoV-2 and COVID-19 Research→French-language works237,207→