Difficulties in reporting purpose and dosage of prescribed medications are associated with poor cognition and depression
Bibliographic record
Abstract
Knowledge on prescribed medication is important for medication adherence. We determined the presence of cognitive impairment in neurological patients who report not to know reasons and dosages of their medication. Data from 350 patients were collected: sociodemographic data, German Stendal Adherence to Medication Score (SAMS), Montreal Cognitive Assessment (MoCA), and Beck Depression Inventory-II (BDI-II). Eighty-eight (29.0%) patients did not know the reasons for taking their prescribed medication and 83 (27.4%) did not know the doses. Sixty-three (20.8%) knew neither reasons nor dosage. The latter were characterized by higher nonadherence, higher number of prescribed medication per day, lower MoCA, higher BDI, and had more often a lower education level compared with patients who knew the reasons. The MANOVA revealed a significant multivariate effect for not knowing the reasons and not knowing the dosages of medication on MoCA and BDI. Significant univariate effects for not knowing reasons were found for depressive mood, but not for cognitive performance. Significant univariate effects for not knowing dosages were found for cognitive performance, but not for depressive mood. Inaccurate medication reporting is not solely associated with cognitive problems, but also with depression, which has to be taken into account in daily practice and research.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".