Medication regimen complexity and the care of the chronically ill patient
Bibliographic record
Abstract
As the population in developed countries ages, patients with multiple chronic conditions are becoming more common. These patients are increasingly being managed with multiple concurrent medications and their medication regimens are frequently described as complex. Despite the significant challenges that complexity poses for clinical decision-making, the adherence of patients to their medication regimens and patient health and wellbeing, a robust understanding of this term in the context of medication regimens, is lacking. Here, it is shown that the essential feature of complex medication regimens is the multiplicity of rules that constitute their basic structure, rather than their intrinsic comprehensibility. Medication regimen complexity is a measure of the size of the consolidated medication script, or the shortest possible list of rules, for that medication regimen. A protocol is suggested for the consolidation of a medication regimen and the measurement and reduction of regimen complexity. This involves simplifying dosing instructions, consolidating the rules for taking medications, determining the number of rules in the consolidated medication script and eliminating or modifying rules towards a more parsimonious treatment plan. Following this protocolmay reduce the burden on the patient associated with adhering to the treatment regimen and thus promote patient-centred outcomes, such as improved health and quality of life, key components of the general move towards person-centered medicine.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".