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Record W3119003936 · doi:10.1016/j.jpeds.2020.12.077

Predicting Adverse Outcomes for Shiga Toxin–Producing Escherichia coli Infections in Emergency Departments

2021· article· en· W3119003936 on OpenAlexafffund
Chu Yang Lin, Jianling Xie, Stephen B. Freedman, Ryan S McKee, David Schnadower, Phillip I. Tarr, Yaron Finkelstein, Neil Desai, Roni D. Lane, Kelly R. Bergmann, Ron L. Kaplan, Selena Hariharan, Andrea T. Cruz, Daniel M. Cohen, Andrew Dixon, Sriram Ramgopal, Elizabeth C. Powell, Jennifer Kilgar, Kenneth A. Michelson, Martin Bitzan, Kenneth Yen, Garth Meckler, Amy C. Plint, Fran Balamuth, Stuart Bradin, Serge Gouin, April Kam, James A. Meltzer, Tracy E. Hunley, Usha Avva, Robert Porter, Daniel M. Fein, Jeffrey P. Louie, Gillian A.M. Tarr, Annie Rominger, Darcy Beer, Christopher M. Pruitt, Thomas J. Abramo, Abigail Schuh, John T. Kanegaye, Neira Jones

Bibliographic record

VenueThe Journal of Pediatrics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsMemorial University of NewfoundlandMcMaster UniversityMcMaster Children's HospitalCentre Hospitalier Universitaire Sainte-JustineChildren's Hospital of Eastern OntarioUniversity of British ColumbiaUniversity of OttawaMcGill University Health CentreUniversity of TorontoMontreal Children's HospitalWestern UniversityAlberta Children's HospitalStollery Children's HospitalUniversité de MontréalHospital for Sick ChildrenBC Children's HospitalSickKids FoundationChildren’s Health Research InstituteUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health ResearchNational Institute of Diabetes and Digestive and Kidney DiseasesCumming School of Medicine, University of CalgaryAgency for Healthcare Research and QualityNational Institutes of Health
KeywordsMedicineAdverse effectDialysisInternal medicinePediatricsSeverity of illnessArea under the curveEmergency 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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.309
Teacher spread0.291 · 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

Citations7
Published2021
Admission routes2
Has abstractno

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