Burn Registries State of Affairs: A Scoping review
Bibliographic record
Abstract
Registry science allows for the interpretation of disease-specific patient data from secondary databases. It can be utilized to understand disease and injury, answer research questions, and engender benchmarking of quality-of-care indicators. Numerous burn registries exist globally, however, their contributions to burn care have not been summarized. The objective of this study is to characterize the available literature on burn registries. The authors conducted a scoping review, having registered the protocol a priori. A thorough search of the English literature, including grey literature, was carried out. Publications of all study designs were eligible for inclusion provided they utilized, analyzed, and/or critiqued data from a burn registry. Three hundred twenty studies were included, encompassing 16 existing burn registries. The most frequently used registries for peer-reviewed publications were the American Burn Association Burn Registry, Burn Model System National Database, and the Burns Registry of Australia and New Zealand. The main limitations of existing registries are the inclusion of patients admitted to burn centers only, deficient capture of outpatient and long-term outcome data, lack of data standardization across registries, and the paucity of studies on burn prevention and quality improvement methodology. Registries are an invaluable source of information for research, delivery of care planning, and benchmarking of processes and outcomes. Efforts should be made to stimulate other jurisdictions to build burn registries and for existing registries to be improved through data linkage with administrative databases, and by standardizing one international minimum dataset, in order to maximize the potential of registry science in burn care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.044 | 0.044 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".