The Systemic Immune-Inflammation Index (SII) and Neutrophil-Lymphocyte Ratio (NLR) are related to hospitalisation time in paediatric burn patients
Bibliographic record
Abstract
Background.The Systemic Immune-Inflammation Index (SII) and Neutrophil-Lymphocyte Ratio (NLR) are new markers of the immune response.There are few studies about the usefulness of these markers in the paediatric population with burns.Objectives.The aim of the study was to reveal the differences in the Systemic Immune-Inflammation Index (SII) and Neutrophil-Lymphocyte Ratio (NLR) in paediatric patients treated due to thermal burn. Material and methods.The study group consisted of 61 children (19 girls, 42 boys; mean age: 3.76; SD 4.79; min-max: 2 months -17 years of age) treated due to thermal burn in the Paediatric Surgery Department.Analysis of chosen complete blood cell count parameters (leucocytes -WBC; platelets -PLT; Systemic Immune-Inflammation Index -SII; Neutrophil-Lymphocyte Ratio -NLR) collected on the day of injury was used as biomarkers of inflammation in patients with and without wound cooling after injury.Results.Children with burns < 5 years of age who had higher PLT values on the day of admission (the day of injury) more frequently required surgical treatment (p = 0,027).Children with more extensive burn wounds (exceeding 10% TBSA) had higher WBC values on the day of the injury (p = 0.034).Higher NLR and SII values were related to longer hospitalisation (p < 0.05). Conclusions.The SII and NLR seem to be promising prognostic markers in children with burns.Further studies on larger groups are necessary to reveal the relationship of the new inflammatory markers with other aspects of burn injury in the paediatric population.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".