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Validation of a Dyspnea Visual Analog Scale in Fibrotic Interstitial Lung Disease

2024· article· en· W4391543210 on OpenAlexaff
Luka Bevanda, Valerie Mok, Kenny Lin, Deborah Assayag, Jolene H. Fisher, Kerri A. Johannson, Nasreen Khalil, Martin Kolb, H. Manganas, Veronica Marcoux, Mohsen Sadatsafavi, Alyson W. Wong, Christopher J. Ryerson

Bibliographic record

VenueAnnals of the American Thoracic Society · 2024
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British ColumbiaUniversity of SaskatchewanMcGill UniversityCentre Hospitalier de l’Université de MontréalUniversity of CalgarySt. Paul's HospitalMcMaster UniversityUniversity of Toronto
FundersBoehringer Ingelheim
KeywordsMedicineVisual analogue scaleInterstitial lung diseaseIdiopathic pulmonary fibrosisLungLung diseasePathologyInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Abstract Rationale A visual analog scale (VAS) is a simple and easily administered tool for measuring the impact of disease; however, little is known about the use of a dyspnea VAS in interstitial lung disease (ILD). Objectives To validate the use of a dyspnea VAS in a large and heterogeneous cohort of patients with fibrotic ILD, including its minimal clinically important difference (MCID), responsiveness to change, and prognostic significance. Methods Patients with fibrotic ILD were identified from a large prospective registry. The validity of a 100-mm dyspnea VAS was assessed by testing its correlation in change score with other measures of ILD severity, including the University of California San Diego Shortness of Breath Questionnaire, the King’s Brief Interstitial Lung Disease quality of life questionnaire Breathlessness and Activities Domain, the European Quality of Life VAS, forced vital capacity, and diffusing capacity of the lung for carbon monoxide. The responsiveness of the dyspnea VAS was qualitatively confirmed on the basis of there being an observable difference in the change in dyspnea VAS across tertiles of change in anchor variables. The MCID in dyspnea VAS was calculated using both anchor (linear regression) and distribution (one-half standard deviation) approaches, with anchors including the above variables that had a correlation with dyspnea VAS (|r| ≥ 0.30). The association of dyspnea VAS with time to death or transplant was determined. Results The cohort included 826 patients with fibrotic ILD, including 127 patients with follow-up measurements at 6 months. The mean baseline dyspnea VAS was 53 ± 24 mm. Dyspnea VAS change scores were moderately correlated with the University of California San Diego Shortness of Breath Questionnaire (|r| = 0.55) and the King’s Brief Interstitial Lung Disease quality of life questionnaire Breathlessness and Activities Domain (|r| = 0.44) and weakly correlated with the European Quality of Life VAS (|r| = 0.19), forced vital capacity percent predicted (|r| = 0.21), and diffusing capacity of the lung for carbon monoxide percent predicted (|r| = 0.05). The MCID was 2.7 to 4.5 using the more reliable anchor-based methods and 12.0 based on distribution-based methods. Dyspnea VAS was associated with time to death or transplant in unadjusted models and after adjustment for age and sex (hazard ratios, 1.16 and 1.15, respectively; P < 0.05 for both). Conclusions This study provides support for the use of the dyspnea VAS in patients with fibrotic ILD, with an estimated anchor-based MCID of 5 mm.

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)0.016
metaresearch head score (Gemma)0.032
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 score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.035
GPT teacher head0.391
Teacher spread0.356 · 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".

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Citations3
Published2024
Admission routes1
Has abstractyes

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