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Record W2105668149 · doi:10.1186/s12916-015-0403-7

Models of disease behavior in idiopathic pulmonary fibrosis

2015· article· en· W2105668149 on OpenAlexaff
Kerri A. Johannson, Brett Ley, Harold R. Collard

Bibliographic record

VenueBMC Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsSouth Health CampusUniversity of Calgary
Fundersnot available
KeywordsMedicineIdiopathic pulmonary fibrosisBiomarkerClinical trialNatural historyDiseaseCohortIntensive care medicineInternal medicineLung

Abstract

fetched live from OpenAlex

Idiopathic pulmonary fibrosis is a diffuse parenchymal lung disease of unknown cause. The natural history of disease can vary considerably, making it difficult to predict the clinical trajectory for an individual patient. Accurate prognostication is desirable for clinical management as well as for cohort enrichment in clinical trials of therapeutics. Clinical and biomarker models of disease behavior have been developed to improve prognostication in idiopathic pulmonary fibrosis, with moderate predictive capabilities. Integrated prediction models that combine both clinical and biomarker variables will improve prognostication for patients and improved cohort enrichment strategies for clinical trials. This goal may be best achieved through collaborative patient registries with prospectively collected biological samples that allow for characterization of disease behavior in idiopathic pulmonary fibrosis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.098
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.301
Teacher spread0.243 · 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 teacher head, 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

Citations9
Published2015
Admission routes1
Has abstractyes

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