Cosmetic surgeries regulations: a comparative legal study between Malaysia, Canada and China / Nur Atiqah Hamran, Umi Kalsum Mat Zuhri and Nur Aminatul Zahriah Khairolanwar
Bibliographic record
Abstract
In the field of cosmetic surgery in Malaysia, legislation needs to be enacted as soon as possible to govern the fast growing industry. This is due to the fact that, the lack of such laws has triggered the blooming of illegal surgeons and beauty salons. In view of an increase in botched surgeries, stern actions need to be taken by the authorities against doctors who performed such procedures despite not being qualified. The current guidelines on aesthetic medicine need to be legislated into law so that it can be legally enforced and regulated. Presently the Health Ministry applies the Private Healthcare Facilities and Services Act (PHFSA) 1998 and the Medical Act 1971 if untrained and unqualified doctors conduct cosmetic surgeries. However, by looking at the increasing cases that involved botched surgery, it seems like the law is not sufficient.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".