Periorbital cellulitis in the pediatric population: clinical features and management of 117 cases.
Bibliographic record
Abstract
OBJECTIVE: Since the advent of the Haemophilus influenzae type B vaccine, no North American case series has described periorbital cellulitis extensively as the main focus in the otolaryngology literature has been the management of orbital abscesses. The aim of this study was to describe the epidemiology, underlying causes, clinical presentation, and medical management of periorbital cellulitis. STUDY DESIGN: Retrospective observational case series. SETTING: Montreal Children's Hospital, McGill University Health Centre, a tertiary pediatric referral center in Montreal, Quebec. SUBJECTS AND METHODS: The medical charts of all pediatric patients hospitalized from January 2000 to August 2006 with a discharge ICD-9 code indicating a diagnosis of periorbital cellulitis without abscess were reviewed. One hundred seventeen cases were identified. RESULTS: Periorbital cellulitis in the pediatric population affects mainly preschool-aged patients (mean age 4.1 ± 4.2 years [SD]) and is more frequent among males than females (1.3:1). The most common predisposing conditions are sinusitis (24.8%), trauma (23.1%), and ocular conditions (13.7%). All patients were successfully managed with intravenous antibiotics for a mean period of 3.4 ± 2.6 days and oral antibiotics for 8.1 ± 4.4 days and recovered fully without complications. CONCLUSION: Our results are consistent with the literature on the subject and show that timely identification of periorbital cellulitis cases and appropriate medical management result in resolution of the condition without complications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".