Estimating the accuracy of optic nerve sheath diameter measurement using a pocket-sized, handheld ultrasound on a simulation model
Bibliographic record
Abstract
BACKGROUND: Ultrasound measurement of optic nerve sheath diameter (ONSD) appears to be a promising, rapid, non-invasive bedside tool for identification of elevated intra-cranial pressure. With improvements in ultrasound technology, machines are becoming smaller; however, it is unclear if these ultra-portable handheld units have the resolution to make these measurements precisely. In this study, we estimate the accuracy of ONSD measurement in a pocket-sized ultrasound unit. METHODS: Utilizing a locally developed, previously validated model of the eye, ONSD was measured by two expert observers, three times with two machines and on five models with different optic nerve sheath sizes. A pocket ultrasound (Vscan, GE Healthcare) and a standard portable ultrasound (M-Turbo, SonoSite) were used to measure the models. Data was analyzed by Bland-Altman plot and intra-class correlation coefficient (ICC). RESULTS: The ICC between raters for the SonoSite was 0.878, and for the Vscan was 0.826. The between-machine agreement ICC was 0.752. Bland-Altman agreement analysis between the two ultrasound methods showed an even spread across the range of sheath sizes, and that the Vscan tended to read on average 0.33 mm higher than the SonoSite for each measurement, with a standard deviation of 0.65 mm. CONCLUSIONS: Accurate ONSD measurement may be possible utilizing pocket-sized, handheld ultrasound devices despite their small screen size, lower resolution, and lower probe frequencies. Further study in human subjects is warranted for all newer handheld ultrasound models as they become available on the market.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".