Co‐morbidity of ‘Clinical Trial’ versus ‘Real‐World’ patients using cardiovascular drugs
Bibliographic record
Abstract
PURPOSE: To examine discrepancies between co-morbidity of patients included in pre-marketing clinical trials of cardiovascular drugs and patients from daily practice, representing the actual users after marketing, and to investigate the availability of data regarding co-morbidity in registration files. METHODS: Data were collected from phase III trials of registration files of 16 drugs, registered in the Netherlands in the period 1985 through 1994 for the indications hypertension, angina pectoris or hypercholesterolemia, and from a general practitioners database. Patients were selected who used drugs from the same therapeutic classes for the same indication as the patients in the pre-marketing trials. Prevalences of concomitant cardiovascular, endocrine and metabolic diseases were compared between pre- and postmarketing populations. Discrepancies were defined as more than 10% difference in prevalences. RESULTS: Data regarding co-morbidity were present in 13 out of 16 registration files and differed in format of reporting. For all indications, coexisting cardiovascular, endocrine and metabolic diseases were less prevalent in the pre-marketing populations, except ischemic heart disease, which was more prevalent coexisting with angina pectoris and hypercholesterolemia. Discrepancies were found for hypertensive disease, heart failure, diabetes mellitus and myocardial infarction. CONCLUSIONS: Phase III trials testing cardiovascular drugs included patients with concomitant cardiovascular, endocrine and metabolic diseases, but discrepancies were present with patients in daily practice. Development of guidelines for uniform collection and reporting of co-morbidity data in pre-marketing trials is recommended, as well as further utilization of data.
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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 | 0.026 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.002 | 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".