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Record W4377985980 · doi:10.2196/44802

Characteristics of Interstitial Pneumonia With Autoimmune Features (IPAF): Protocol for a Multicenter Prospective Study

2023· article· en· W4377985980 on OpenAlexvenueno aff
Patrycja Rzepka-Wrona, Szymon Skoczyński, Wojciech Piotrowski, Ewa Jassem, Dariusz Ziora, Adam Barczyk

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInterstitial lung diseaseInternal medicineIdiopathic interstitial pneumoniaBronchoalveolar lavageUsual interstitial pneumoniaMixed connective tissue diseaseRheumatoid arthritisLung

Abstract

fetched live from OpenAlex

BACKGROUND: "Interstitial lung disease" (ILD) is a broad term encompassing diseases of different backgrounds. "Interstitial pneumonia with autoimmune features" (IPAF) is a recent term that implies the presence of autoimmunity. OBJECTIVE: This study aims to determine the characteristics of Polish patients with IPAF, compare them with patients with other interstitial pneumonias, and search for the prognostic and diagnostic biomarkers of IPAF in serum and bronchoalveolar lavage fluid (BALF). METHODS: This multicenter prospective study plans to recruit 240 participants divided into 1 study group and 2 control groups. Biological fluid samples will be collected according to Polish Respiratory Society management guidelines and stored at -80°C for further tests. Prospective 5-year observations of 60 newly diagnosed individuals are planned. The study will be divided into subsections. First, we plan to characterize Polish patients with IPAF (study group) against their peers with other ILDs (2 control groups). Control group 1 will comprise patients with idiopathic ILDs, including mainly idiopathic pulmonary fibrosis and nonspecific interstitial pneumonia. Control group 2 will comprise patients with connective tissue disease-associated interstitial lung diseases, such as rheumatoid arthritis, systemic sclerosis, polymyositis, dermatomyositis, Sjögren's syndrome, mixed connective tissue disease, and systemic lupus erythematosus. Radiological and functional parameters will be analyzed. Patients will be compared in terms of high-resolution computed tomography results, the 6-minute walking test performance, and pulmonary function test parameters. The diagnosis of IPAF will be reassessed on a regular basis through multidisciplinary discussion in order to determine its clinical stability. In the laboratory arm, inflammation and fibrosis pathways will be assessed. Cytokine levels (interleukin 8, transforming growth factor beta 1, chemokine C-C motif ligand [CXCL]18, CXCL1, surfactant protein [SP]-A, SP-D, Krebs von den Lungen-6 protein, and chitinase 1) will be measured in serum and BALF. A comparative analysis of serum and BALF cytokine levels will be performed in order to establish potential differences between systemic and local inflammatory pathways. In the quality of life (QoL) arm of the study, dyspnea and cough and their impact on various aspects of the QoL will be assessed. Depression and anxiety will be measured with the Hospital Anxiety and Depression Modified Scale and the 9-item Patient Health Questionnaire, and potential correlations with symptom prevalence will be assessed. RESULTS: This study will start recruiting patients to phase 1 in October 2023. The final results will be available in 2028. We plan to publish preliminary results after 2-3 years from the start of phase 1. CONCLUSIONS: This study will be a step toward a better understanding of IPAF etiopathogenesis and outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44802.

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)0.022
metaresearch head score (Gemma)0.014
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.023
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.014
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.003
Science and technology studies0.0030.001
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0230.005

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 head0.124
GPT teacher head0.511
Teacher spread0.387 · 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
GenreProtocol

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

Citations2
Published2023
Admission routes1
Has abstractyes

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