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Record W3027006948 · doi:10.5489/cuaj.6693

Prioritization and management recommendations of paediatric urology conditions during the COVID-19 pandemic

2020· article· en· W3027006948 on OpenAlexaffvenue
Daniel T. Keefe, Mandy Rickard, Peter Anderson, Darius Bägli, Anne‐Sophie Blais, Stéphane Bolduc, Luis H. Braga, Natasha Brownrigg, Michael Chua, Sumit Davé, Joana Dos Santos, Luis María López Guerra, Allen Hayashi, Mélise Keays, Soojin Kim, Martin A. Koyle, Linda Lee, Armando J. Lorenzo, Dawn L. MacLellan, Landan MacDonald, Andrew E. MacNeily, Peter Metcalfe, Katherine M. Moore, Rodrigo Romao, Peter Wang

Bibliographic record

VenueCanadian Urological Association Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsUniversity of AlbertaChildren's Hospital of Eastern OntarioUniversity of OttawaWestern UniversityVictoria General HospitalMcMaster UniversityStollery Children's HospitalIzaak Walton Killam Health CentreCentre hospitalier universitaire de QuébecSickKids FoundationDalhousie UniversityUniversity of British ColumbiaHospital for Sick Children
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Prioritization2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineIntensive care medicineVirologyBusinessInternal medicineProcess managementInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

that can be referenced by primary care providers, as well as general and pediatric urologists, in order to help standardize the care of pediatric urology patients during the pandemic and provide guidance on managing the surge of patients once restrictions begin to be lifted.

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)0.037
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: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.001
Science and technology studies0.0050.001
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0190.003

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 head0.029
GPT teacher head0.270
Teacher spread0.241 · 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
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

Citations13
Published2020
Admission routes2
Has abstractyes

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