Correlation Between Conditional Approval and Customized Bone Implant Devices
Bibliographic record
Abstract
This report aims to summarize key concerns regarding customized devices and conditional approval during the premarket evaluation of bone implants, and to explore the correlation between them. Based on the experience of approval of the first domestic custom-designed bone implant, we consider the process of gaining conditional approval for urgently-needed medical devices and medical devices for rare diseases, as well as the guidance available for clinical investigation. We also streamlined the scientifically administrative concept of this unique device, from the design and development of premarket technical evaluation to continuous post-market study. The present study found that those two aspects have certain connections, but they are not directly correlated to each other. In contrast to the USA, Canada, Australia and the EU, where regulations and guidelines have been established for the use of customized devices, in this regard, China is still it its infancy. Thus, there is considerable potential for China to develop and perfect the policies relating to customized devices and to develop relevant strategies to ensure their efficacy with the aid of conditional approval. Appropriate scientific conditional approval for mass production of individualized anatomy-matching bone implants could become a valuable approach for precision medicine.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".