The Use of Ultrasound-Measured Optic Nerve Sheath Diameter to Predict Ventriculoperitoneal Shunt Failure in Children
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to assess the accuracy of ultrasound-measured optic nerve sheath diameter (ONSD) as a screen for ventriculoperitoneal shunt failure. METHODS: We prospectively enrolled a convenience sample of children presenting to the ED with suspected shunt failure. The ONSD was measured by ultrasound and compared with computed tomography/magnetic resonance imaging (CT/MRI) and neurosurgical impression. We defined shunt failure on ultrasound as an ONSD greater than 4.0 mm in infants 12 months and younger or greater than 4.5 mm in children older than 12 months. A single emergency radiologist at our institution read all CTs and MRIs for categorical determination of shunt failure. We defined shunt failure based on neurosurgical impression as a decision to admit and perform shunt revision. We report test characteristics and 95% confidence intervals of ONSD as a predictor for shunt failure. RESULTS: We enrolled 32 subjects. The sensitivities of ONSD compared with CT/MRI and neurosurgical impression, 60.0% and 75.0%, respectively, were low. However, the negative predictive values of ONSD compared with CT/MRI and neurosurgical impression were 90.0% and 95.0%, respectively. CONCLUSIONS: Optic nerve sonography may be a useful tool to identify children presenting with suspected ventriculoperitoneal shunt failure who do not require further imaging. This would reduce the use of CT scan and exposure to ionizing radiation in children with suspected shunt malfunction who do not require neurosurgical intervention. Consideration of additional risk factors and a larger sample size may yield stronger results.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".