A Process to add Long-Term Outcomes into the American Burn Association’s Burn Registry – Feasibility to Bridge the Gap
Bibliographic record
Abstract
BACKGROUND: Burn registries play a crucial role in enhancing the understanding of burn epidemiology and improving clinical care. However, they often lack comprehensive data on post-discharge outcomes when patients transition to outpatient care. This study aimed to initiate the expansion of the American Burn Association's registry to include long-term outcomes for patients receiving outpatient follow-up post-discharge. MATERIALS AND METHODS: The Quality of Burn Registry Outpatient Work Group identified nine key long-term outcomes-five clinical and four psychosocial-to track after discharge from burn centers. An alpha pilot study was conducted with seven verified burn centers, collecting data on enrolled patients over 12 months in three-month intervals. A subsequent beta pilot involved ten centers, each monitoring five patients across five predefined cohorts. RESULTS: The alpha pilot enrolled 29 patients, revealing variable documentation and data retrieval times of up to 15 minutes per patient. The beta pilot encompassed 200 patients and recorded 1417 appointments, averaging 7.1 visits per patient. Notably, 25% of patients were lost to follow-up, and 22% were discharged from care within 12 months. Follow-up visits were most concentrated in the first three months (53.6%). DISCUSSION: This study represents a pioneering effort to systematically collect long-term outcomes for major burn injury survivors during the first year post-discharge. The findings will support ongoing improvements in best practices for burn care and enhance continuity between inpatient and outpatient monitoring, ultimately benefiting quality improvement initiatives for burn-injured patients in the future.
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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 | 0.402 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".