Primary drug non-adherence in dermatology patients: prevalence, pattern and reasons
Bibliographic record
Abstract
<p class="abstract"><strong>Background:</strong> Primary drug non-adherence, a pervasive problem in dermatology is not readily documented despite the adverse effect of this phenomenon on the management of patients. The aim of the study was to document the prevalence, pattern and reasons for primary drug non-adherence in dermatology patients.</p><p class="abstract"><strong>Methods:</strong> This prospective cross-sectional questionnaire based study was conducted from June to December 2021 amongst 302 consecutive consenting adult patients returning to the dermatology clinic following an initial diagnosis and a prescription for medications. Data collected included socio-demographic parameters, primary drug adherence, and number of drugs prescribed, number not purchased, and reason for non-adherence. Data analysis was done in R Studio [R Core Team (2021)].<strong></strong></p><p class="abstract"><strong>Results:</strong> Three hundred and two (302) patients aged 13 to 87 years with a mean age of 41.72±18.8 years were recruited into the study. Prevalence of primary non-drug adherence was 26.2% (79/302). Amongst the non-adherent patients; 73.4% were females and 26.6% were males. The reasons for non-drug adherence ranged from non-availability of drug (63.3%) to patient forgetting about the prescription. (1.3%). Route of drug not adhered to was Topical in 72%, Oral in 22.7%, oral and topical in 5.3.</p><p class="abstract"><strong>Conclusions:</strong> Primary drug non-adherence is common with dermatology patients. The propensity for primary non-drug adherence is increased when the number of drugs prescribed are more than three. Dermatologists need to consider the use of drugs capable of addressing multiple symptoms and thereby reduce the number of drugs prescribed. </p><p class="abstract"> </p>
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".