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Record W4281552814 · doi:10.1016/j.chest.2022.05.021

Development and Internal Validation of a Prognostic Model of the Probability of Death or Lung Transplantation Within 2 Years for Patients With Cystic Fibrosis and FEV1 ≤ 50% Predicted

2022· article· en· W4281552814 on OpenAlexaff
Kathleen J. Ramos, Travis Hee Wai, Anne L. Stephenson, Jenna Sykes, Sanja Stanojevic, Patricia J Rodriguez, Aasthaa Bansal, Nicole Mayer-Hamblett, Christopher H. Goss, Siddhartha G. Kapnadak

Bibliographic record

VenueCHEST Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsHospital for Sick ChildrenSt. Michael's Hospital
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteU.S. Food and Drug AdministrationUnited Network for Organ SharingNational Cancer InstituteNational Institutes of HealthNational Center for Advancing Translational SciencesGovernment of South AustraliaCystic Fibrosis Foundation
KeywordsMedicineCystic fibrosisInternal medicineLogistic regressionLung transplantationTransplantation

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)0.025
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.029
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.023
GPT teacher head0.271
Teacher spread0.248 · 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 designSimulation or modeling
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

Citations8
Published2022
Admission routes1
Has abstractno

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Same venueCHEST JournalSame topicCystic Fibrosis Research AdvancesFrench-language works237,207