Factors associated with phenotypes of dyspnea in post-COVID-19 condition: a cross-sectional study
Bibliographic record
Abstract
Abstract Post-COVID-19 condition (PCC) is defined as the persistence of symptoms, like fatigue and dyspnea, at least 3 months post-COVID infection. As dyspnea is a common symptom, we attempted to further clinically phenotype those with PCC-associated dyspnea. 1642 adults (average age of 49.6y with 63% female-predominance and BMI of 31.2 kg/m2) with physician confirmed diagnosis of PCC from June 2020–April 2023 in Alberta, Canada were included. Those with dyspnea were more likely to be female (56.5%, p = 0.005) and have higher BMI (31.3 kg/m2 vs. 29.5 kg/m2; p = 0.0008), history of asthma (21.1% vs. 12.3%; p < 0.001), more persistent PCC symptoms (p = 0.0001), more functional limitations, as well as lower quality of life (p < 0.0001). Multivariable-adjusted logistic regression analysis demonstrated dyspnea was independently associated with fatigue (OR = 4.20; CI = 2.71,6.59) and inversely associated with hospitalization for COVID-19 (OR = 0.53; CI = 0.32,0.91), age (OR = 0.98 per one year of age; CI = 0.96,0.99) and 6-min-walk-distance per 10 m difference (OR = 0.98, CI = 0.96,1.0). Fatigue was a predictor of dyspnea, and was associated with milder infection, higher BMI, and reduced 6-min-walk-distance despite normal pulmonary function. Reduced TLC or DLCO was associated with more severe infection and reduced 6-min-walk-distance. Thus, we speculate there are at least two dyspnea-associated phenotypes: phenotype with pronounced fatigue (normal PFT) and phenotype with pronounced pulmonary abnormalities (abnormal PFT). Improved understanding of the dyspnea-associated phenotypes may allow for better targeted rehabilitation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".