746 Routinely Collected Burn Clinical Data in Canada: Determining the Knowledge Gap
Bibliographic record
Abstract
Abstract Introduction Unlike other developed countries that hold national burn registries to monitor burn injury and care, Canada relies on single-centre secondary datasets and administrative databases as surveillance mechanisms. The objective of this study is to determine the knowledge gap faced in Canada for not having a dedicated burn registry. Methods A comprehensive scoping review was conducted to identify the burn literature that has arisen from secondary datasets in Canada. Literature of all study designs was included with the exception of case reports and cases series. Once data extraction was concluded, a thematic framework was constructed based on the information that arose from nations that hold national burn registries. Results Eighty-eight studies were included. Twelve studies arose from national datasets, and 18 from provincial databases, most of which were from Ontario and British Columbia. Only seven studies were conducted using a combination of Canadian units’ single-centre datasets. The majority of included studies (58%) resulted from non-collaborative use of single-centre secondary datasets. Research efforts were predominantly conducted by burn units in Ontario, British Columbia, Manitoba and Alberta. A significant number of the included studies were outdated and there were several provinces/territories with no published burn data whatsoever. Conclusions Efforts should be made towards the development of systems to surveil burn injury and care in Canada. This study supports the development of a working group and potentially a nation-wide burn registry to bridge this knowledge gap. Applicability of Research to Practice Decisions regarding burn injury prevention and delivery of burn care must be informed by clinical data, in order to maximize efficacy and outreach. Canada has a publicly funded healthcare system, and yet, these decisions at present are not informed by data. The establishment of the knowledge gap that is faced by the Canadian burn community is the first step towards the establishment of a working group that will discuss how Canada will move forward to bridge this gap and increase the quality of burn care delivered across the country.
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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 | 0.085 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.032 | 0.052 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".