A Systematic Review of Quality Improvement Interventions in Burn Care
Bibliographic record
Abstract
Quality improvement interventions (QIIs) are intended to improve the care of patients. Unlike most traditional clinical research, these endeavors emphasize the sustainable implementation of scientific evidence rather than the establishment of evidence. Our purpose was to conduct a systematic review of QII publications in the field of burn care. A systematic review was conducted utilizing electronic databases (MEDLINE, Embase, and Cochrane Library) of all studies relating to "quality improvement" in burn care published until March 31, 2020. Studies were excluded if no baseline data were reported, or if no intervention was applied and tested. Studies were scored using a novel 10-point evaluation system for QII. We evaluated 414 studies involving "quality improvement" in burn care. Only 82 studies contained a QII while 332 studies were categorized as traditional research. Several traditional research studies made claims to be QIIs, but few met the criteria. Of the 82 QII references, only 20 (24%) were accessible as full-text manuscripts, the remainder were published as abstracts only. The mean score was 7.95 for full-text studies (range 6-10) and 7.4 for abstract-only studies (range 5.5-9.5). Despite the importance of quality improvement (QI) in burn care, very few studies have been published that employ true QI methodology, and many QII studies never advance beyond publication as abstracts in conference proceedings. Based on this systematic review, we propose guidelines to improve the quality of QII submissions.
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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 | 0.265 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".