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Record W2987881408 · doi:10.1016/j.jcf.2019.10.006

Antimicrobial resistance in cystic fibrosis: A Delphi approach to defining best practices

2019· article· en· W2987881408 on OpenAlexafffund
Edith T. Zemanick, Pierre‐Régis Burgel, Giovanni Taccetti, Alison Holmes, Félix Ratjen, Catherine A. Byrnes, Valerie J. Waters, Scott C. Bell, Donald R. VanDevanter, J.S. Elborn, Patrick A. Flume

Bibliographic record

VenueJournal of Cystic Fibrosis · 2019
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institutes of HealthNational Center for Advancing Translational SciencesCystic Fibrosis in AustraliaNational Institute for Health and Care ResearchCystic Fibrosis CanadaSouth Carolina Clinical and Translational Research Institute, Medical University of South CarolinaUniversity of South CarolinaMedical University of South CarolinaCystic Fibrosis TrustCystic Fibrosis Foundation
KeywordsMedicineCystic fibrosisDelphi methodIntensive care medicineFamily medicineMEDLINEPediatricsInternal 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)0.241
metaresearch head score (Gemma)0.200
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.936

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2410.200
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.004
Science and technology studies0.0110.010
Scholarly communication0.0090.008
Open science0.0040.021
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.323
Teacher spread0.296 · 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

Citations32
Published2019
Admission routes2
Has abstractno

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