Medical tourism: Medical dream or nightmare?
Bibliographic record
Abstract
Medical tourism is an act of a foreign national traveling across international borders to undergo a particular medical procedure. India, Malaysia, Turkey, Singapore, and Thailand have established themselves as prominent destinations in the medical tourism industry, while Canada and the United Kingdom are commonly sought-after source countries for medical travelers. The medical tourism sector offers a range of benefits and drawbacks for citizens of both the source country and the destination country. Traveling long distances for medical treatment presents inherent risks such as complications during travel and exposure to unfamiliar environments. For instance, patients may face an increased risk of developing deep vein thrombosis (DVTs) due to prolonged periods of immobility during flights or other modes of transportation. The most popular desired procedures for medical tourists include elective cosmetic surgery, dental procedures, organ transplants, cardiac surgery, and orthopaedic surgery.Medical tourism is double-edged in nature and can be both beneficial to the health system and the recipient thereof. It is however clear that various international legal and regulatory frameworks are necessitated to both protect the interests of the medical tourist and the treating medical body. There is no question that medical tourism will continue to rise in popularity, it is however prudent that potential medical tourists do their due diligence into the potential destination for their treatment of choice to better nullify the likelihood of a medical misadventure occurring.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".