Prescription and Non-Prescription Drug Classification Systems Across Countries: Lessons Learned for Thailand
Bibliographic record
Abstract
Doungporn Leelavanich,1 Noppadon Adjimatera,2,3 Lawanworn Broese Van Groenou,4 Puree Anantachoti1 1Department of Social and Administrative Pharmacy, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, Thailand; 2Thailand Self Medication Industry Association, Bangkok, Thailand; 3Reckitt Benckiser (Thailand) Ltd, Bangkok, Thailand; 4DKSH (Thailand) Limited, Bangkok, ThailandCorrespondence: Puree AnantachotiDepartment of Social and Administrative Pharmacy, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, ThailandTel/Fax +66 89-441-8456Email puree.a@chula.ac.thPurpose: The drug classification system, as prescription or non-prescription drug category, has been utilized as a regulatory strategy to ensure patient safety. In Thailand, the same system has been used for decades, though the drug classification criteria were updated to accommodate drug re-classification in 2016. These new criteria, however, have not been applied retroactively. Inconsistency in drug classification has been observed leading to concerns regarding the drug classification system. This has prompted the need for a review of the drug classification system in Thailand. This study aims to explore Thailand and other selected countries’ regulatory management regarding the drug classification system, drug classification criteria, and drug classification itself.Methods: The drug classification systems of the United States, the United Kingdom, Japan, Singapore, Malaysia, the Philippines, and Canada were selected to study alongside Thailand’s system. The regulatory review was conducted through each country’s drug regulatory agency website and available published research. Complementary interviews with drug regulatory authorities were conducted when written documentation was unclear and had limited access. Fifty-two common drugs were selected to compare their actual classifications across the different countries.Results: All selected countries classified drugs into two major groups: prescription drugs and non-prescription drugs. The studied countries further sub-classified non-prescription drugs into 1– 4 categories. Principles of drug classification criteria among countries are similar; they comprised of three themes: disease characteristics, drug safety profile, and other drug characteristics. Actual drug classification of antibiotics, dyslipidemia treatments, and hypertension treatments in Thailand are notedly different from other countries. Furthermore, 77.4% of drugs studied in Thailand fall into the behind-the-counter (dangerous) drug category, which varied from antihistamines to antibiotics, dyslipidemia treatments, and vaccines.Conclusion: Thailand’s drug classification criteria are comparable with other nations; however, there is a need to review drug classification statuses as many drugs have been classified into improper drug categories.Keywords: drug category, classification criteria, prescription, non-prescription, OTC, drug regulatory
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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 | 0.029 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".