Compliance descriptors: Analysis and evaluation in terms of therapeutic effect
Bibliographic record
Abstract
The aim of this work was to evaluate the performance of various compliance parameters in order to identify those which best assess the impact of compliance on therapeutic issues. We will discuss the particularities and restrictions of these parameters by considering two criteria, namely sensitivity index and reliability, which respectively describe strength and robustness of the relationship between these parameters and compliance. Using real and virtual data, performance analysis of compliance parameters was carried out for drugs whose pharmacokinetic properties govern the time course of their actions. Within this context, it was found that the percentage of taken doses (PTD), the most widely used parameter, poorly performed in the evaluation of the therapeutic impact of compliance. On the other hand, the adjusted percentage of correct doses (PCD*) which we propose here, showed the best reliability, making it the most appropriate parameter for the comparison of different compliance patterns. The percentage of correct doses (PCD) has, in its turn, the highest sensitivity index and thus should be preferred for the assessment of changes in compliance. Hence, a perfect parameter for the evaluation of compliance impact cannot be universally identified since each parameter can have its own characteristic advantages and limitations. The methodology proposed here is general enough to be adapted for similar drug classes to evaluate their compliance descriptors.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".