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Record W4412995367 · doi:10.2196/64409

Aspects of Quality of Life in Interstitial Lung Disease: Pilot Observational Cross-Sectional Study in a Single Center

2025· article· en· W4412995367 on OpenAlexvenueno aff
Patrycja Rzepka-Wrona, Marzena Trzaska-Sobczak, Adam Barczyk, Szymon Skoczyński

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsnot available
FundersUniwersytet Śląski w Katowicach
KeywordsObservational studyPreprintCross-sectional studyMedicineCenter (category theory)Quality (philosophy)Internal medicinePathologyPhysicsComputer scienceChemistryWorld Wide Web

Abstract

fetched live from OpenAlex

Background: Quality of life (QOL) is an important aspect of every chronic disease, including interstitial lung disease (ILD). QOL is perceived as a significant patient-centered outcome. Objective: This study aims to identify factors correlating with different aspects of QOL in patients with various ILDs. Methods: We recruited 57 participants hospitalized in a tertiary care clinical center to this pilot observational cross-sectional study. These included 22 patients with idiopathic interstitial pneumonia (IIP), 19 patients with connective tissue disease-associated ILD (CTD-ILD), and 16 patients with interstitial pneumonia with autoimmune features (IPAF). The Saint George's Respiratory Questionnaire (SGRQ) and World Health Organization Quality of Life Questionnaire (WHOQOL-BREF) were used to assess QOL, and the Hospital Anxiety and Depression Scale - Modified Version (HADS-M) and Patient Health Questionnaire - 9 (PHQ-9) were used to evaluate depression severity. Functional parameters including forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), transfer lung capacity for carbon monoxide (TLCO), and 6-minute walk distance (6MWD) were assessed. Assessment of QOL was a secondary outcome measure in a multicenter prospective study aimed at determining the characteristics of Polish patients with interstitial pneumonia with autoimmune features. Results: In each study group, positive correlations existed between the WHOQOL-BREF physical domain score and FEV1 % predicted value (P=.001) and TLCO % predicted value (P=.03). Regardless of diagnosis, higher depression, anxiety, and aggression scores (ie, worse mental health) correlated negatively with multiple domains of QOL measured using the WHOQOL-BREF. Predictors of QOL aspects varied in each study group. In the IPAF group, the TLCO % predicted value was a predictor of QOL expressed as the SGRQ total score (P=.005). In the CTD-ILD group, short 6MWD (P<.001) and high HADS-M aggression score (P=.01) correlated with low QOL (expressed as a high SGRQ total score). In the IIP group, 6MWD (P=.002) and PHQ-9 scores (P<.001) were predictors for SGRQ symptoms score. Gender-based differences were revealed: In all study groups, men had higher scores in the psychological, social, and environmental domains of the WHOQOL-BREF, indicating better QOL, without a statistically significant difference in the physical domain scores between genders. Diagnosis-based differences in the psychological aspects of QOL were also revealed: The QOL psychological domain scores were significantly lower in the CTD-ILD and IPAF groups than in the IIP group, indicating worse QOL (P=.01). Conclusions: QOL is a multifaceted issue with various factors impacting its assessment. 6MWD, TLCO predicted value, and worse functional ability might specifically impact QoL in ILD. Mental health is an important aspect of QOL in the ILD population, as patients with a chronic, potentially life-limiting disease may be more prone to developing depression or anxiety. Assessment of QOL should be taken into account in clinical decision-making and research on chronic diseases, as this patient-related outcome may impact therapeutic decisions and patient compliance.

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)0.003
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.140
GPT teacher head0.468
Teacher spread0.328 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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