What are the factors related to misdiagnosis of COPD?
Bibliographic record
Abstract
Background: The factors for COPD misdiagnosed by physicians are not known. Objective: To determine the factors associated with COPD misdiagnosed. Methods: This research was part of the Canadian Cohort Obstructive Lung Disease (CanCOLD). Subjects were recruited (population-based sampling) from 9 cities. Physician-diagnosed COPD was based on patient self-reported. COPD was confirmed by spirometry, i.e., post-BD FEV 1 /FVC <0.70. Results: This analysis included 2132 subjects from 5 cities. Of 163 with physician-diagnosed COPD, 79 were confirmed to have COPD by spirometry while 84 didn't have COPD, 333 had COPD confirmed by spirometry but were undiagnosed, 910 were at risk (ever smoker) and 726 were healthy (never smoker). Among those with physician-diagnosed COPD as compared to undiagnosed COPD, diagnosed subjects were more likely to be current smokers (36% vs 20%, p<0.0001), to have chronic bronchitis (32% vs 12%, p<0.0001), wheezing (64% vs 38%, p<0.0001), dyspnea ≥3/5 MRC (22% vs 9%, p<0.0001), diagnosis of asthma (47% vs 23%, p<0.0001), and lower health status. Similar characteristics were present for physician-diagnosed COPD whether or not the diagnosis was confirmed by spirometry. Predictors of physician-diagnosed COPD included current smoking (OR: 1.86, 95% CI: 1.09-3.18), chronic cough (2.04, 1.13-3.69), chronic bronchitis (2.70, 1.45-5.04), and reduced physical health “SF-12” (0.96, 0.96-0.99). Conclusions: Misdiagnosis and underdiagnosis of COPD is common. Current smoking, respiratory symptoms and reduced health seems to trigger physician to make diagnosis of COPD. The absence of these factors may result in underdiagnosis. Funding: CIHR Rx&D Collaborative Research Program; and the Respiratory Health Network of the FRSQ.
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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 | 0.003 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".