Systematic analysis of self-reported comorbidities in the COSYCONET COPD cohort study by stepwise evaluation of medication
Bibliographic record
Abstract
Background: In large cohort studies comorbidities are commonly self-reported by the patients. Although this is a feasible way to collect information, it only represents conditions memorized. In order to improve the use of all available information, we developed a detailed procedure to compare self-reported comorbidities with medication and applied this to the data of the German COPD cohort COSYCONET. Methods: Approach I was based solely on ICD-10-codes for the diseases and the indications of medications. To deal with the non-specificity of medications, Approach II focused on disease-specific medication and ATC-codes. The relationship between comorbidities and medication was expressed by a four-level concordance score. Results: Approach I and II demonstrated that the patterns of concordance scores markedly differed between diseases. On average, Approach I resulted in more than 50% concordance of all reported diseases to at least one medication. Approach II showed particularly large differences in its ability of matching with medications, due to large differences in the disease-specificity of drugs, e.g. for diabetes versus specific cardiovascular disorders. Conclusion: Both approaches provide defined strategies to confirm self-reported diagnoses via medication. Approach I covers a broad spectrum of diseases and medications but is limited regarding disease-specific information. Approach II is based on medications specific for a disease and can reach higher concordance. The strategies described are generally applicable in large studies to extract as much information as possible from the available data. Funded by BMBF COSYCONET and Mundipharma GmbH.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".