A Unique Model for Ultrasound Assessment of Optic Nerve Sheath Diameter
Bibliographic record
Abstract
BACKGROUND: Ultrasonic assessment of optic nerve sheath diameter (ONSD) as a non-invasive measure of intracranial pressure (ICP) has been evaluated in the literature as a potential valid technique for rapid ICP estimation in the absence of invasive intracranial monitoring. The technique can be challenging to perform and little literature exists surrounding intra-operator variability. OBJECTIVES: In this study we describe the creation of a novel model of ONSD to be utilized in ultrasound training of this technique. We demonstrate the realistic ultrasonographic images created utilizing this novel model. METHODS: We designed ocular models composed of gelatin spheres and variable three dimensional printed cylinders, which simulate the globe of the eye and variable ONSD's respectively. These models were suspended in a gelatin background and ultrasound of the ONSD was conducted using standard techniques described in the literature. RESULTS: This model produces clear and accurate representation of ONSD that closely mimics in vivo images. It is affordable and easy to produce in large quantities, portending its use in an educational environment. CONCLUSIONS: Utilizing the standard linear array ultrasound probe for ONSD measurements in our model provided realistic images comparable to in vivo. This provides an affordable and exciting means to test intra- and inter- operator variability in a standardized environment. Knowing this, we can further apply this novel model of ONSD to ultrasound teaching and training courses with confidence in its ability and the technique's ability to produce consistent 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".