Visual Analog Scales for Assessment of Dyspnea, Cough, and Quality of Life in Interstitial Lung Disease: A Systematic Review
Bibliographic record
Abstract
Abstract Rationale: Patient-reported outcome measures (PROMs) are used in many areas of medicine to assess disease symptoms. However, these PROMs pose challenges in clinical settings due to their barriers such as complexity, time to complete, language and cultural barriers. An alternative approach to symptoms assessment is the visual analogue scale (VAS), a simpler tool to quantify the severity of symptoms that has already been validated in several diseases such as asthma and COPD. Our study aims to assess the validity of VAS in symptom assessment and define the change on the VAS that corresponds to meaningful clinical changes, to support their use in clinical practice or research in ILD. Methods: A comprehensive literature search was carried out in the Embase and Medline databases for original studies published between January 1, 2000 and May 10, 2024 that are related to the use of VAS for assessment of ILD symptoms: dyspnea, cough, and quality of life (QOL). Two independent reviewers screened the publications based on title and abstract review, followed by full-text review, which resulted 18 studies that were selected for this systematic review. Results: The VAS assessing dyspnea, cough, and QOL showed significant correlations with other PROMs assessing these ILD symptoms. Dyspnea VAS measuring dyspnea severity in ILD correlated with other PROMs assessing dyspnea such as UCSD SOBQ, modified Borg Scale, K-BILD, SGRQ as well as with PROMs assessing QOL such as HAQ-DI and EQ-VAS. Cough VAS also showed correlation with PROMs measuring cough and QOL, as well as objective measures of respiratory function such as FVC and DLCO. Likewise, QOL VAS correlated with QOL and dyspnea PROMs. MCID for VAS dyspnea ranged from 3.3 mm to 22.0 mm and dyspnea QOL ranged from 0.5 to 9.7 mm. The MCID for VAS Cough could not be assessed due to weaker and inconsistent data. Conclusions: This systematic review highlights the potential of VAS tools as effective and streamlined measures for assessing dyspnea, cough, and quality of life in patients with ILD. Our review suggests VAS correlates well with other validated patient-reported outcome measures, demonstrating both validity and clinical relevance. Given their simplicity and ease of use, VAS tools could enhance routine clinical practice and clinical trials, ultimately improving patient management in ILD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".