Factors associated with undiagnosed and overdiagnosed COPD
Bibliographic record
Abstract
Worldwide, studies have shown that about 60–86% of people with chronic obstructive pulmonary disease (COPD) have not been diagnosed, which represents a missed opportunity to decrease disease burden through optimal management, including smoking cessation support and prescription medications [1–3]. Overdiagnosed COPD is also common, with prevalence estimates ranging from 4% to 64% in the general population and primary care settings [4, 5]. Overdiagnosis can lead to unnecessary COPD treatments with their own risks and costs, poor health-related quality of life and missed detection and treatment of other diseases [6]. Several factors are associated with undiagnosed and overdiagnosed COPD Parts of this material are based on data and information compiled and provided by the Canadian Institute for Health Information (CIHI). However, the analyses, conclusions, opinions and statements expressed herein are those of the authors, and not necessarily those of CIHI. Author contributions were as follows. A.S. Gershon, J. Hwee, J.C. Victor and T. To conceived and designed the study. A.S. Gershon, J. Hwee and J.C. Victor acquired the health administrative data and D.E. O'Donnell, K.R. Chapman, S.D. Aaron, J. Bourbeau and W. Tan acquired the clinical data. All authors designed the study. J. Hwee and J. Su carried out the statistical analysis. All authors interpreted the data. A.S. Gershon and J. Hwee drafted the manuscript. All authors critically revised the manuscript for important intellectual content and approved the submitted version. A.S. Gershon obtained funding. A.S. Gershon, J. Hwee and J.C. Victor were involved in administrative support. A.S. Gershon, J. Hwee and J. Su had full access to all the data in the study and take responsibility for the integrity of the data and accuracy of the analysis. A.S. Gershon is guarantor.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".