Changes in the Management of Severe Orbital Infections Over Seventeen Years
Bibliographic record
Abstract
OBJECTIVES: Periorbital and orbital cellulitis are common but serious infections in children. Management of these infections varies because of an absence of clinical guidelines, but it is unclear if management within institutions has changed over time. We compared the management and outcomes of children hospitalized with periorbital and orbital cellulitis in 2 eras. METHODS: Data were extracted from records of children hospitalized at a tertiary care children’s hospital with periorbital or orbital cellulitis from 2000 to 2005 and 2012 to 2016. Patient demographics, cross-sectional imaging, antibiotic and corticosteroid use, length of stay, and surgical rates were collected. Data from the eras were compared by using descriptive statistics, t tests, Mann–Whitney U tests, Fisher’s exact tests, and χ2 tests. RESULTS: There were 318 children included, 143 from 2000 to 2005 and 175 from 2012 to 2016. Compared with the first era, in the second era there were increased rates of MRI (5% vs 11%, P = .04), although rates of computed tomography scan use remained unchanged (60% vs 65%); increased number (1 vs 3, P < .01) and spectrum of antibiotics; increased use of intranasal corticosteroids (3% vs 49%, P < .01); and subspecialty consultation (89% vs 99%, P = .01). There were no differences in length of stay, readmission, or surgical rates between eras. CONCLUSIONS: There has been considerable change in the management of hospitalized children with severe orbital infections at our institution, including the rates of MRI, number and spectrum of antibiotics used, use of adjunctive agents, and increased subspecialty involvement with no observed impact on clinical outcomes. Future research is needed to rationalize antimicrobial therapy and reduce low-value health care.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| 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".