Adherence to medication in adults with attention deficit hyperactivity disorder and pro re nata dosing of psychostimulants: A systematic review
Bibliographic record
Abstract
Adherence to a regular medication regimen may be challenging for adults with attention deficit hyperactivity disorder (ADHD). Some report taking psychostimulants on a pro re nata (PRN) basis. This review aims to establish the rate of adherence, and reasons for and consequences of non-adherence to medication for ADHD in adults, and to review literature on PRN dosing of psychostimulants in these patients. A systematic literature search was conducted. Four primary research studies have investigated the rate of adherence to medication in adults with ADHD. Mean adherence rate in two studies ranged from 52% to 87%. A number of possible reasons for poor adherence have been suggested. Prospective studies are needed to further define the rate of adherence and causes of poor adherence. Evidence examining whether differences in adherence affect clinical outcomes is equivocal. Therefore, caution should be applied to the assumption that maximising adherence to regular medication regimens will improve clinical outcomes. Two articles acknowledge that patients take medication on a PRN basis. Studies comparing the effectiveness of a regular and PRN regimen of psychostimulants are needed. If PRN dosing is as effective as a regular regimen, advantages might include enhanced doctor-patient communication, reduced side effects and cost savings.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".