A New Inexpensive Simulation Model for Ultrasound Assessment of Optic Nerve Sheath Diameter
Bibliographic record
Abstract
BACKGROUND: Sonographic measurement of optic nerve sheath diameter (ONSD) is becoming increasingly accepted as a diagnostic modality to detect elevations in intracranial pressure. As this technique becomes more widespread, methods to address the inherent operator-dependent nature of this modality will need to be developed. We propose a novel low-cost model to accurately simulate sonographic ONSD measurement for purposes of training and assessment. METHODS: We designed models composed of medical tubing of various diameters readily available from typical hospital supplies and suspended them in gelatin. The models were evaluated by ultrasound by three expert point-of-care sonographers using a standard linear array probe and technique proposed in the literature. RESULTS: This model generates faithful simulation of the ONS that closely approximates in vivo images and can be used to produce accurate, reproducible measurements. Materials are low cost and easy to acquire and assemble. CONCLUSIONS: Our model provides realistic simulated images of the ONS. Through comparison of sonographic measurements to the known tube diameters, this model serves as a promising inexpensive tool to teach the method of ultrasound assessment of ONSD or as a way to determine accuracy of this novel ultrasound technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".