Bibliographic record
Abstract
Objectives Main objective of this research was to research medical device legislation in international distance selling through a case company. One of the key objectives was to build a tool for business decision making, based on the differences in medical device legislation in EU, USA, Australia, Canada, and South Korea. The current state of global harmonization of medical device legislation is also researched. Summary This thesis mainly focused on literature review. The reviewed five markets were chosen to benefit the case company, Unikulma Oy. The research problem covers how medical device legislation affects different parties in distance selling, but the main focus remains on businesses. Conclusions Harmonization of medical device legislation is currently on a relatively high level in the five researched markets. International Medical Device Regulators Forum is currently the main organization working towards more harmonized medical device legislation globally. Distance selling of medical devices, where consumer imports medical devices for personal use, is not allowed in all markets, and businesses need to be aware of this. Further harmonizing medical device legislation globally would likely benefit businesses, but has other limitations and problems, that need to be taken into account before continuing the harmonization of medical device legislation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.012 |
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".