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Record W2808526795 · doi:10.1016/j.jamda.2018.05.001

The Development of a Decision Tool for the Empiric Treatment of Suspected Urinary Tract Infection in Frail Older Adults: A Delphi Consensus Procedure

2018· article· en· W2808526795 on OpenAlexaff
Laura W. van Buul, Hilde L. Vreeken, Suzanne Bradley, Christopher J. Crnich, Paul J. Drinka, Suzanne E. Geerlings, Robin Jump, Lona Mody, Joseph J. Mylotte, Mark Loeb, David A. Nace, Lindsay E. Nicolle, Philip D. Sloane, Rhonda L. Stuart, Pär‐Daniel Sundvall, Peter Ulleryd, Ruth B. Veenhuizen, Cees M.P.M. Hertogh

Bibliographic record

VenueJournal of the American Medical Directors Association · 2018
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsUniversity of ManitobaMcMaster University
FundersNational Institute on AgingAgency for Healthcare Research and QualityMinisterie van Volksgezondheid, Welzijn en Sport
KeywordsMedicineUrinalysisDelphi methodLeukocyte esteraseIntensive care medicineEmpiric therapyPopulationUrinary systemEmpiric treatmentInternal medicineAntibioticsPediatricsPathologyAlternative 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)0.327
metaresearch head score (Gemma)0.294
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.327
Threshold uncertainty score0.830

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3270.294
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0100.003
Science and technology studies0.0060.004
Scholarly communication0.0060.005
Open science0.0040.012
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.318
Teacher spread0.305 · 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.

Study designQualitative
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

Citations79
Published2018
Admission routes1
Has abstractno

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