Prescribing Pattern for Skin Diseases in Dermatology OPD at Borumeda Hospital, North East, Ethiopia
Bibliographic record
Abstract
Introduction: Skin diseases are the major contributors of disease burden in society. Dermatological therapy ultimate goal is achieved by administering the safest and least number of drugs. The problem gets compounded with the inappropriate and irrational use of medicines. Therefore, periodic prescription audit in the form of prescribing patterns is away to improve irrational prescription. The objective of this study to assess the prescription patterns of dermatological agents in Borumeda hospital. Method: Hospital based retrospective cross sectional study in which prescribing patterns of dermatological agents are assessed. A total of 385 samples of patient record prescription from November/1/2016 to December/30/2016, and the sample were selected by systematic random sampling technique. Sample prescriptions were reviewed using structural data collection format. The Collected data was analyzed by using SPSS version 20. Result: Regarding rout of administration, the maximum number of drugs was prescribed topically (66.2%). Topical steroids were the most commonly prescribed drugs (25.3%). Use of generic prescribing in single drug prescribing was 81.7%. The prevalence of atopic dermatitis was higher (26.3%, 20.8%) in both male and female respectively followed by scabies in male with 12.2% and Acne vulgaris (12.9%) in female. Number of drugs per prescription was higher (2.46) than WHO standard (<2). Conclusion: The current study reveals that topical corticosteroids were commonly prescribed drugs in the dermatology unit and the prescribing practice imitates incidence of polypharmacy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".