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Record W3042014887 · doi:10.1007/s00247-020-04713-1

A primer for pediatric radiologists on infection control in an era of COVID-19

2020· review· en· W3042014887 on OpenAlexaff
Monica Miranda‐Schaeubinger, Einat Blumfield, Govind B. Chavhan, Amy Farkas, Aparna Joshi, Shawn E. Kamps, Summer L. Kaplan, Marla B. K. Sammer, Elizabeth Silvestro, A. Luana Stanescu, Raymond W. Sze, Danielle M. Zerr, Tushar Chandra, Emily A. Edwards, Naeem Khan, Eva I. Rubio, Chido D. Vera, Ramesh S. Iyer

Bibliographic record

VenuePediatric Radiology · 2020
Typereview
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsIzaak Walton Killam Health CentreHospital for Sick Children
FundersNational Institute of General Medical Sciences
KeywordsMedicinePersonal protective equipmentInfection controlCoronavirus disease 2019 (COVID-19)Pediatric RadiologyPandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Best practice2019-20 coronavirus outbreakPediatric Infectious DiseaseMedical emergencyTransmission (telecommunications)Medical physicsIntensive care medicinePathologyInfectious disease (medical specialty)Disease

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)0.006
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0040.009
Open science0.0010.003
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0120.004

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 head0.122
GPT teacher head0.455
Teacher spread0.333 · 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
GenreReview

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

Citations5
Published2020
Admission routes1
Has abstractno

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