Adherence to Referral Criteria for Burn Patients; a Systematic Review.
Bibliographic record
Abstract
Introduction: Burn injuries are under-appreciated trauma, associated with substantial morbidity and mortality. It is necessary to refer patients in need of specialized care to more specialized centers for treatment and rehabilitation of burn injuries. This systematic review aimed to assess the adherence to referral criteria for burn patients. Methods: An extensive search was conducted on Scopus, PubMed, and Web of Science online databases using the relevant keywords from the earliest to October 7, 2021. The quality of the included studies was assessed using the appraisal tool for cross-sectional studies (AXIS tool). Results: Among a total of 7,455 burn patients included in the nine studies, 60.95% were male. The most frequently burned areas were the hands (n=3) and the face (n=2). The most and least common burn mechanisms were scalds (62.76%) and electrical or chemical (2.88%), respectively. 51.88% of burn patients had met ≥ 1 referral criteria. The overall adherence to the referral criteria for burn patients was 58.28% (17.37 to 93.39%). The highest and lowest adherence rates were related to Western Cape Provincial (WCP) (26.70%) and National Burn Care Review (NBCR) (4.97%) criteria, respectively. Conclusion: The overall adherence to the referral criteria for burn patients was relatively desirable. Therefore, well-designed future studies are suggested in order to uncover approaches to improve adherence to referral criteria for burn patients.
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 | 0.035 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".