Compliance descriptors: Analysis and evaluation in terms of therapeutic effect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it