Lung disease burden assessment by oscillometry in a systematically disadvantaged urban population experiencing homelessness or at-risk for homelessness in Ottawa, Canada from a prospective observational study
Bibliographic record
Abstract
Abstract Rationale Oscillometry is an emerging technique that offers some advantages over spirometry as it does not require forced exhalation and may detect early changes in respiratory pathology. Obstructive lung disease disproportionately impacts people experiencing homelessness with a high symptoms burden, yet oscillometry is not studied in this population. Objectives To assess lung disease and symptom burden using oscillometry in people experiencing homelessness or at-risk of homelessness using a community-based participatory action research approach (The Bridge Model™). Methods Of 80 recruited, 55 completed baseline oscillometry, 64 completed spirometry, and all completed patient-reported outcomes with demographics, health, and respiratory symptom related questionnaires in the Participatory Research in Ottawa: Management and Point-of-Care for Tobacco Dependence project. Using a two-tail t-test, we compared mean oscillometry values for airway resistance (R 5–20 ), reactance area under the curve (A x ) and reactance at 5 Hz ( X 5 ) amongst individuals with fixed-ratio method (FEV 1 /FVC ratio < 0.70) and LLN (FEV 1 /FVC ratio ≤ LLN) spirometry diagnosed chronic obstructive pulmonary disease (COPD). We compared mean oscillometry parameters based on participants’ COPD assessment test (CAT) scores using ANOVA test. Results There was no significant difference between the pre- and post- bronchodilator values of R 5–20 and A x for the fixed ratio method ( p = 0.63 and 0.43) and the LLN method ( p = 0.45 and 0.36). There was a significant difference in all three of the oscillometry parameters, R 5–20 , A x and X 5 , based on CAT score ( p = 0.009, 0.007 and 0.05, respectively). There was a significant difference in R 5–20 and A x based on the presence of phlegm ( p = 0.03 and 0.02, respectively) and the presence of wheeze ( p = 0.05 and 0.01, respectively). Oscillometry data did not correlate with spirometry data, but it was associated with CAT scores and correlated with the presence of self-reported symptoms of phlegm and wheeze in this population. Conclusions Oscillometry is associated with respiratory symptom burden and highlights the need for future studies to generate more robust data regarding the use of oscillometry in systematically disadvantaged populations where disease burden is disproportionately higher than the general population. Trial Registration : ClinicalTrails.gov —NCT03626064, Retrospective registered: August 2018, https://clinicaltrials.gov/ct2/show/NCT03626064
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".