Diagnostic Instability and Reversals of Chronic Obstructive Pulmonary Disease Diagnosis in Individuals with Mild to Moderate Airflow Obstruction
Bibliographic record
Abstract
Abstract Rationale Chronic obstructive pulmonary disease (COPD) is a chronic, progressive disease, and reversal of COPD diagnosis is thought to be uncommon. Objectives To determine whether a spirometric diagnosis of mild or moderate COPD is subject to variability and potential error. Methods We examined two prospective cohort studies that enrolled subjects with mild to moderate post-bronchodilator airflow obstruction. The Lung Health Study (n = 5,861 subjects; study duration, 5 yr) and the Canadian Cohort of Obstructive Lung Disease (CanCOLD) study (n = 1,551 subjects; study duration, 4 yr) were examined to determine frequencies of (1) diagnostic instability, represented by how often patients initially met criteria for a spirometric diagnosis of COPD but then crossed the diagnostic threshold to normal and then crossed back to COPD over a series of annual visits, or vice versa; and (2) diagnostic reversals, defined as how often an individual’s COPD diagnosis at the study outset reversed to normal by the end of the study. Measurements and Main Results Diagnostic instability was common and occurred in 19.5% of the Lung Health Study subjects and 6.4% of the CanCOLD subjects. Diagnostic reversals of COPD from the beginning to the end of the study period occurred in 12.6% and 27.2% of subjects in the Lung Health Study and CanCOLD study, respectively. The risk of diagnostic instability was greatest for subjects whose baseline FEV1/FVC value was closest to the diagnostic threshold, and the risk of diagnostic reversal was greatest for subjects who quit smoking during the study. Conclusions A single post-bronchodilator spirometric assessment may not be reliable for diagnosing COPD in patients with mild to moderate airflow obstruction at baseline.
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 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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".