Predictors for Admission of Children With Periorbital Cellulitis Presenting to the Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVES: To identify demographic and clinical characteristics associated with admission because of periorbital cellulitis (PC) in children. METHODS: Records of children aged 0 to 18 years with PC who visited our tertiary emergency department (ED) in 2004 were reviewed. We calculated a cumulative number of local ocular symptoms in patients that included swelling/edema, redness/erythema, presence of discharge, pain, conjunctival injection, and shut eye. A binary logistic regression analysis was performed to identify predictors of admission for PC. RESULTS: A total of 89 children were included in the analysis; 39 (44%) of them were admitted to the ward. A cumulative number of local symptoms associated with PC and temperature in the ED served as significant predictors of hospitalization (odds ratio, 2.5; P = 0.005; and odds ratio, 2.0; P = 0.04, respectively). Among individual local symptoms, only swelling/edema was found to significantly predict admission in univariate analysis (P = 0.03). Considerable variation was documented in intravenous and oral antibiotics prescribed in the ED. CONCLUSIONS: Combination of local ocular symptoms and body temperature are positively associated with admission from the ED. Future research should concentrate on evaluating the suggested score we used in this cohort to validate it and evaluate its generalizability. Devising such scoring can help clinicians determine guidelines for admission of children with PC.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".