Oscillometry from the CanCOLD Cohort: correlation with spirometry and patient reported outcomes
Bibliographic record
Abstract
Oscillometry (OS) offers faster and easier objective measures of lung function than spirometry (SP). We wished to characterize OS parameters within the CanCOLD cohort. 126 subjects from the Montreal CanCOLD site completed PROs (mMRC, COPD Assessment Tests, SGRQ), and performed SP (Medisoft, Sorinnes, Belgium) and OS (tremoFlo C-100, Thorasys, Canada) at the same visit. Subjects were divided into subgroups of Normal, AtRisk, GOLD1 and GOLD2+ as previously reported. Between group differences were sought using ANOVA and post hoc t-tests and associations between SP, PROs and ln transformed OS parameters (R5, R5-19, X5, AX) using Bonferroni corrected pairwise correlations. All GOLD2+ OS parameters differed from other groups; AX the most (median, IRQ: 20, 9, 29 cmH20/L vs. GOLD1 5, 2, 10; AtRisk 5, 2, 10; Normal 3, 2, 6, p<0.001 for all). Of SP parameters, FEV1 demonstrated the strongest correlations with OS. Of OS parameters, AX demonstrated the strongest correlations with SP (Table 1). The mMRC demonstrated the strongest correlation between PROs and both SP (FEV1 r=-0.46, FEV1% r=-0.49, FEV1/FVC r=-0.46, p<0.001 for all) and OS (R5 r=0.42, R5-19 r=0.33, X5 r=0.32 and AX r=0.40, p<0.001 for all). Our results suggest of OS parameters, AX may merit prospective exploration as an alternative to the FEV1 for diagnosis and management of COPD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".