Day-to-day variability of inspiratory resistance: A sensitive and specific marker of asthma
Bibliographic record
Abstract
Rationale: It is known that day-to-day variability of lung function is increased in asthma but it is also increased in COPD (Timmins et al. 2012). Objectives: To identify possible FOT parameter and time window for calculating the day-to-day variability of airway obstruction and compare its accuracy, sensitivity and specificity for identifying asthma from healthy and COPD. Methods: Daily home FOT measurements were collected from 47 mild-to-moderate asthmatics, 20 moderate-to-severe COPD patients and 35 healthy controls. The mean and day-to-day variability (standard deviation, SD, or coefficient of variation, CV) of FOT time-series were then calculated over multiple time windows. PEF variability was calculated as per current recommendations. Measurements and Main Results: A two-week day-to-day variability of inspiratory resistance (CV14Rinsp) allowed an optimal and significant separation of the asthma from the healthy and COPD groups (p<0.05). Within this time window, a linear correlation existed between the mean of inspiratory resistance and its SD, but it was steeper for the asthmatics (0.17±0.03, r2 = 0.49, p<0.001) than healthy (0.08±0.02, r2 = 0.44, p<0.001) and COPD (0.11±0.04, r2 = 0.30, p<0.005) subjects (p=0.19). CV14Rinsp was significantly higher in asthma (0.14±0.05) than both in the healthy (0.07±0.02, p<0.05) and COPD (0.09±0.04, p<0.05) groups. The accuracy of CV14Rinsp for separating asthmatics from healthy subjects was significantly greater than PEF (91% vs. 57%, p<0.001), with increased sensitivity (70% vs. 27%) and specificity (100% vs. 95%). Conclusions: CV14Rinsp is a sensitive and specific marker of asthma.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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 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".