Drug utilization in the Maastricht Study
Bibliographic record
Abstract
Within the southern region of the Netherlands, the Maastricht Study is an on-going observational prospective population-based cohort study that focuses on the etiology of Type 2 diabetes mellitus (T2DM). Representativeness of the participating population is a crucial but often an unknown factor in population-based cohort studies such as the Maastricht Study. We therefore aimed to assess the representativeness of the study population by comparing drug utilization of the participants of the Maastricht Study with the general population of the Netherlands.Since T2DM patients were oversampled in this study, a sampling method was applied in order to ensure a similar distribution of T2DM over the study population. Drug use in the study population was compared with drug use in the population of the Netherlands, using a Z-test to compare 2 independent proportions.In general, drug use in the study was similar compared with national data. However, in the age group 65 to 74 years total drug use was lower in the study population (833/1000 persons) versus nationwide data (882/1000 persons). The use of pulmonary medications was lower (104/1000 persons vs 141/1000 persons) and the use of hypnotics/anxiolytics was higher (90/1000 persons vs 36/1000 persons) in the Maastricht Study as compared with national data.Drug use in the Maastricht Study population is largely comparable to that in the total Dutch population aged 45 to 74. Therefore, data on drug use by participants in the Maastricht Study can be used to perform studies assessing outcomes associated with drug use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".