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Record W2976928218 · doi:10.1080/17476348.2019.1673733

Treatment of idiopathic pulmonary fibrosis with Nintedanib: an update

2019· review· en· W2976928218 on OpenAlexaff
Amornpun Wongkarnjana, Toyoshi Yanagihara, Martin Kolb

Bibliographic record

VenueExpert Review of Respiratory Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsNintedanibMedicinePirfenidoneIdiopathic pulmonary fibrosisTolerabilityIntensive care medicineInterstitial lung diseaseExpert opinionDiseasePulmonologistsInternal medicineAdverse effectLung

Abstract

fetched live from OpenAlex

Introduction: Idiopathic pulmonary fibrosis (IPF) is an incurable, progressive and debilitating disease. Nintedanib is one of two anti-fibrotic therapies available for the treatment of IPF and has been approved since 2014. Together with pirfenidone and antacid medications it has received a conditional recommendation for the treatment for IPF by international clinical practice guidelines.Areas covered: The authors review the mechanisms of action, pharmacological profile and update scientific data and our opinions on efficacy, safety profile and tolerability of nintedanib.Expert opinion: Nintedanib significantly slows disease progression in IPF patients with tolerable and manageable side effects. Its potential future role in the treatment of progressive fibrosing interstitial lung diseases other than IPF is challenging.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.059
GPT teacher head0.384
Teacher spread0.325 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2019
Admission routes1
Has abstractyes

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