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Record W4281287406 · doi:10.1136/gutjnl-2022-327440

Serological responses to three doses of SARS-CoV-2 vaccination in inflammatory bowel disease

2022· letter· en· W4281287406 on OpenAlexafffundabout
Joshua Quan, Christopher Ma, Remo Panaccione, Lindsay Hracs, Nastaran Sharifi, Michelle Herauf, Ante Makovinović, Stephanie Coward, Joseph W. Windsor, Léa Caplan, R Ingram, Jamil N. Kanji, Graham Tipples, Jessalyn K. Holodinsky, Çharles N. Bernstein, Douglas J. Mahoney, Sasha Bernatsky, Eric I. Benchimol, Gilaad G. Kaplan

Bibliographic record

VenueGut · 2022
Typeletter
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsSickKids FoundationUniversity of TorontoUniversity of ManitobaMcGill University Health CentreUniversity of AlbertaInstitute for Clinical Evaluative SciencesUniversity of Calgary
FundersPublic Health AgencyPublic Health Agency of CanadaGilead SciencesCanadian Institutes of Health ResearchLeona M. and Harry B. Helmsley Charitable TrustPfizerAmgen
KeywordsInflammatory bowel diseaseMedicineVaccinationSerologyInflammatory Bowel DiseasesImmunologyCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakDiseaseVirologyInternal medicineAntibodyInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

Canadian Institutes of Health Research Operating Grant: COVID-19 Rapid Research Funding Opportunity funding reference number VR5-172684. Public Health Agency of Canada through the Vaccine Surveillance Reference Group (VSRG) and the COVID-19 Immunity Task Force (CITY). The Leona M and Harry B Helmsley Charitable Trust Grant #G-2209-05501.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0090.003
Insufficient payload (model declined to judge)0.0040.003

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 head0.072
GPT teacher head0.367
Teacher spread0.295 · 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 designObservational
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

Citations19
Published2022
Admission routes3
Has abstractyes

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