569 Burn Registries State of Affairs: A Scoping Review
Bibliographic record
Abstract
Abstract Introduction Registry science applies observational study designs to interpret large secondary databases. It can be utilized to understand disease and injury, answer research questions, inform regulatory decision making, and engender benchmarking of quality-of-care indicators. Numerous burn registries exist globally, however their contributions to the science of burn epidemiology, care and treatment have not been summarized. The objective of this study is to characterize the available literature on burn registries. Methods We conducted a scoping review, having registered the protocol a priori. A comprehensive literature search across several databases, including the grey literature, was carried out. Studies of all methodological designs were included provided they utilized, analyzed, and/or critiqued burn registry data. Pilot projects from registries in development were included as well. Studies involving non-burn specific registries or registries from a single burn centre were excluded. Results Two hundred and sixty-eight studies were included, encompassing 16 existing burn registries. Although registry science has been used to investigate burn care since 1970, the majority of studies were published after 2007. Most studies utilized the American Burn Association Burn Registry or one of its previous versions (75.7%) and the Burns Registry of Australia and New Zealand (10.4%). Main limitations of existing registries are the inclusion of patients admitted to burn centres only, deficient capture of outpatient and long-term outcome data, and lack of data standardization across registries. Conclusions Registries are an invaluable source of data for research, delivery of care planning, and benchmarking of processes and outcomes. Efforts should be made to stimulate other jurisdictions to build and maintain burn registries, to incorporate data linkage from administrative and other secondary databases, and to standardize data collection, in order to maximize the potential of registry science in burn care.
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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 | 0.038 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.051 | 0.046 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".