Radiation Oncology Device Approval in the United States and Canada
Bibliographic record
Abstract
Background Medical devices are a crucial component in the field of radiation oncology. The review and licensing of radiation oncology devices (RODs) is managed on a national basis in Canada by Health Canada and in the United States by the Food and Drug Administration (FDA). The purpose of this study was to examine differences in ROD licensing timelines between Health Canada and the FDA that may impact the ability of Canadians to access the most up-to-date radiation oncology care. Methods A list of ROD was compiled by searching keywords, manufacturers, and proprietary device names in the publicly accessible Canadian Medical Devices Active Licence Listing (MDALL) and the American Establishment Registration & Device Listing and the 510(k) Premarket Notification database. ROD licensing dates were then obtained through both databases. ROD were included if they were licensed in both countries. Results A total of 51 RODs were included in this study and it was found that 71% (36/51) were issued licenses for sale in the United States before Canada, at a mean of 506 days sooner (median [IQR] = 282 [326.5]). No trends in licensing dates were found by stratifying devices by type. Analyses were limited to the date of licensing only, as Health Canada provided no publicly-available information regarding submission milestones such as first submission date for the RODs studied. Conclusions The majority of radiation oncology devices examined were licensed for sale in the USA before Canada. Due to the absence of publicly available information regarding initial ROD application date, we cannot evaluate the impact of the approval process on the overall difference in licensing date. Importantly, this research highlights a lack of publicly-available information from Health Canada regarding the medical device approval process for the radiation oncology devices studied herein.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".