Test Characteristics of Point‐of‐care Ultrasound for the Diagnosis of Retinal Detachment in the Emergency Department
Bibliographic record
Abstract
BACKGROUND: Previous studies of point-of-care ultrasound (POCUS) have reported high sensitivities and specificities for retinal detachment (RD). Our primary objective was to assess the test characteristics of POCUS performed by a large heterogeneous group of emergency physicians (EPs) for the diagnosis of RD. METHODS: This was a prospective diagnostic test assessment of POCUS performed by EPs with varying ultrasound experience on a convenience sample of emergency department (ED) patients presenting with flashes or floaters in one or both eyes. After standard ED assessment, EPs performed an ocular POCUS scan targeted to detect the presence or absence of RD. After completing their ED visit, all patients were assessed by a retina specialist who was blinded to the results of the POCUS scan. We calculated sensitivity and specificity with associated exact binomial confidence intervals (CIs) using the retina specialist's final diagnosis as the reference standard. RESULTS: A total of 30 EPs enrolled 115 patients, with median age of 60 years and 64% female. The retina specialist diagnosed RD in 16 (14%) cases. The sensitivity and specificity of POCUS for detecting RD were 75% (95% CI = 48%-93%) and 94% (95% CI = 87%-98%), respectively. The positive likelihood ratio was 12.4 (95% CI = 5.4-28.3), and negative likelihood ratio was 0.27 (95% CI = 0.11-0.62). CONCLUSIONS: A large heterogeneous group of EPs can perform POCUS with high specificity but only intermediate sensitivity for RD. A negative POCUS scan in the ED performed by a heterogeneous group of providers after a 1-hour POCUS didactic is not sufficiently sensitive to rule out RD in a patient with new-onset flashes or floaters.
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".