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Record W4220900375 · doi:10.1007/s00431-022-04422-x

Infants hospitalized for acute COVID-19: disease severity in a multicenter cohort study

2022· article· en· W4220900375 on OpenAlexaff
Joanna Merckx, Shaun K. Morris, Ari Bitnun, Peter J. Gill, Tala El Tal, Ronald M. Laxer, E. Ann Yeh, Carmen Yea, Rolando Ulloa‐Gutiérrez, Helena Brenes-Chacón, Adriana Yock‐Corrales, Gabriela Ivankovich‐Escoto, Alejandra Soriano‐Fallas, Marcela Hernández-de Mezerville, Jesse Papenburg, Marie‐Astrid Lefebvre, Alireza Nateghian, Behzad Haghighi Aski, Ali Manafi, Rachel Dwilow, Jared Bullard, Suzette Cooke, Tammie Dewan, Léa Restivo, Alison Lopez, Manish Sadarangani, Ashley Roberts, Michelle Barton, Dara Petel, Nicole Le Saux, Jennifer Bowes, Rupeena Purewal, Janell Lautermilch, Sarah Tehseen, Ann Bayliss, Jacqueline Wong, Isabelle Viel‐Thériault, Dominique Piché, Karina A. Top, Kirk Leifso, Cheryl Foo, Luc Panetta, Joan Robinson

Bibliographic record

VenueEuropean Journal of Pediatrics · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversity of AlbertaMemorial University of NewfoundlandQueen's UniversityDalhousie UniversityMcMaster UniversityUniversity of ManitobaTrillium Health CentreUniversité de MontréalUniversity of SaskatchewanMcGill University Health CentreMcGill UniversityUniversity of OttawaUniversity of TorontoWestern UniversityUniversity of British ColumbiaUniversité LavalBC Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicineOdds ratioPediatricsSeverity of illnessDiseaseCohort studyRetrospective cohort studyLogistic regressionConfidence intervalCohortCoronavirus disease 2019 (COVID-19)Intensive careInternal medicineInfectious disease (medical specialty)Intensive care medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.417
Teacher spread0.378 · 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

Citations11
Published2022
Admission routes1
Has abstractno

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