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Record W4284988213 · doi:10.1016/j.jcf.2022.06.012

Cystic fibrosis related diabetes is not associated with maximal aerobic exercise capacity in cystic fibrosis: a cross-sectional analysis of an international multicenter trial

2022· article· en· W4284988213 on OpenAlexafffund
Thomas Radtke, Susi Kriemler, Lothar Stein, Chantal Karila, Don S. Urquhart, David M. Orenstein, Larry C. Lands, Christian Schindler, Ernst Eber, Sarah R. Haile, Helge Hebestreit, Marlies Wagner, Helmut Ellemunter, Nancy Alarie, Clotilde Simon, Anne Faucou, Laurent Mély, Bruno Ravaninjatovo, Anne Prévötat, Jonathan Schaeff, Cordula Koerner‐Rettberg, J. Hammermann, Christina Smaczny, Inka Held, Sibylle Junge, Oliver Nitsche, Rainald Fischer, Jörg Große-Onnebrink, Anne Wesner, Andreas Hector, Alexandra Hebestreit, Christian Benden, Carmen Casaulta, Reta Fischer, Alexander Mœller, Erik H.J. Hulzebos, Marcella Burghard, Sarah Blacklock, Debbie Miller, Z. Johnstone, John Lowman

Bibliographic record

VenueJournal of Cystic Fibrosis · 2022
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsMcGill UniversityMontreal Children's Hospital
FundersAssociation Vaincre la MucoviscidoseCystic Fibrosis CanadaNederlandse Cystic Fibrosis StichtingSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungCystic Fibrosis Foundation
KeywordsMedicineCystic fibrosisCystic fibrosis-related diabetesConfoundingVO2 maxAerobic exerciseAerobic capacityVital capacityInternal medicineDiabetes mellitusCross-sectional studyAnthropometryPhysical therapyType 2 diabetesEndocrinologyImpaired glucose toleranceHeart rateDiffusing capacityBlood pressurePathology

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)0.008
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 score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.302
Teacher spread0.278 · 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
Published2022
Admission routes2
Has abstractno

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