Diagnostic test accuracy of ultrasound for orbital cellulitis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Periorbital and orbital cellulitis are inflammatory conditions of the eye that can be difficult to distinguish using clinical examination alone. Computer tomography (CT) scans are often used to differentiate these two infections and to evaluate for complications. Orbital ultrasound (US) could be used as a diagnostic tool to supplement or replace CT scans as the main diagnostic modality. No prior systematic review has evaluated the diagnostic test accuracy (DTA) of ultrasound compared to cross-sectional imaging. OBJECTIVE: To conduct a systematic review of studies evaluating the DTA of orbital ultrasound compared with cross-sectional imaging, to diagnose orbital cellulitis. METHODS: MEDLINE, EMBASE, CENTRAL, and Web of Science were searched from inception to August 10, 2022. All study types were included that enrolled patients of any age with suspected or diagnosed orbital cellulitis who underwent ultrasound and a diagnostic reference standard (i.e., CT or magnetic resonance imaging [MRI]). Two authors screened titles/abstracts for inclusion, extracted data, and assessed the risk of bias. RESULTS: Of the 3548 studies identified, 20 were included: 3 cohort studies and 17 case reports/series. None of the cohort studies directly compared the diagnostic accuracy of ultrasound with CT or MRI, and all had high risk of bias. Among the 46 participants, diagnostic findings were interpretable in 18 (39%) cases which reported 100% accuracy. We were unable to calculate sensitivity and specificity due to limited data. In the descriptive analysis of the case reports, ultrasound was able to diagnose orbital cellulitis in most (n = 21/23) cases. CONCLUSION: Few studies have evaluated the diagnostic accuracy of orbital ultrasound for orbital cellulitis. The limited evidence based on low quality studies suggests that ultrasound may provide helpful diagnostic information to differentiate orbital inflammation. Future research should focus studies to determine the accuracy of orbital US and potentially reduce unnecessary exposure to radiation.
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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 | 0.019 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".