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Record W2811389906 · doi:10.1183/13993003.02593-2017

Stability or improvement in forced vital capacity with nintedanib in patients with idiopathic pulmonary fibrosis

2018· article· en· W2811389906 on OpenAlexafffund
Martin Kolb, Carlo Vancheri, Wenbo Tang, Craig Conoscenti, Luca Richeldi

Bibliographic record

VenueEuropean Respiratory Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsMcMaster University
FundersVeracyteSanofiCanadian Pulmonary Fibrosis FoundationBiogenGlaxoSmithKlineFibroGenPulmonary Fibrosis FoundationGenentechAstraZenecaGilead SciencesBoehringer Ingelheim
KeywordsNintedanibVital capacityIdiopathic pulmonary fibrosisMedicinePulmonary fibrosisInternal medicineCardiologyIntensive care medicineFibrosisLungLung functionDiffusing capacity

Abstract

fetched live from OpenAlex

analyses of the distribution of changes in FVC in the INPULSIS® trials and FVC changes in the open-label extension trial INPULSIS®-ON in subgroups of patients based on whether patients had shown an improvement or no decline in FVC in INPULSIS®. Analyses were descriptive.Based on the annual rate of change in FVC, 158 of 638 patients (24.8%) treated with nintedanib and 38 of 423 patients (9.0%) treated with placebo had an improvement/no decline in FVC in the INPULSIS® trials. In patients whose FVC improved/did not decline, median (interquartile range) improvements in FVC at week 52 were 76.5 (31-152) mL and 57.5 (31-103) mL in the nintedanib and placebo groups, respectively. Changes in FVC from baseline to week 48 of INPULSIS®-ON were similar in patients whose FVC improved or declined in the preceding INPULSIS® trial.In the INPULSIS® trials, treatment with nintedanib resulted in a greater proportion of patients with IPF showing an improvement/no decline in FVC compared to taking placebo. Mechanisms underlying improvement in FVC in patients with IPF are unknown.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.232
Teacher spread0.213 · 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

Citations41
Published2018
Admission routes2
Has abstractyes

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