Primary drug non-adherence in dermatology patients: prevalence, pattern and reasons
Bibliographic record
Abstract
Background: 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. Methods: 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)]. Results: 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. Conclusions: 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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".