Publishing Quality Improvement Interventions in Burn Care: A Call to Frontline Clinicians
Bibliographic record
Abstract
To the Editor, Quality improvement (QI) is increasingly recognized as central to advancing burn care, ensuring not only the generation of new knowledge but the sustainable application of evidence in real clinical environments. In 2021, we published a systematic review evaluating the extent and attributes of Quality Improvement Interventions (QIIs) in burn care. That review identified 414 studies related to improvement, yet only 82 met core criteria for QI, and just 20 were published as full-text manuscripts; the remaining interventions were predominantly presented as abstracts and never progressed to peer-reviewed publication.1 Without publication, the knowledge and gains from these efforts remain local, slowing the spread of safer, more effective care practices across burn centres. Since that review, QI work has continued to expand, driven by verification standards, morbidity and mortality processes, audit and feedback culture, and increasingly interdisciplinary engagement. However, the translation gap persists: many clinically successful QI initiatives are not disseminated, resulting in lost opportunities for system-level learning and shared advancement.
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 | 0.109 | 0.495 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.030 | 0.035 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".