Agreement Between Computed Tomography And Magnetic Resonance Imaging In Measuring Optic Nerve Sheath Diameter
Bibliographic record
Abstract
BACKGROUND: Neuroimaging is increasingly used as a non-invasive method to assess raised intracranial pressure (ICP). Optic nerve sheath diameter (ONSD) measurement using brain magnetic resonance imaging (MRI) has been shown to correlate well with invasively measured ICP, however little research has been conducted on the ONSD measurement using computerized tomography (CT) in correlation with ICP. This study was done to investigate whether CT scan can reliably replace MRI in measuring ONSD.METHOD: A cross-sectional comparative study was conducted on 50 adult patients (29 females and 21 males), who underwent both CT and MRI of the brain along 10-month period. Using the brain axial section, the transverse ONSD was measured at 3 mm behind the globe in both modalities. Agreement between CT and MRI readings was assessed using intraclass correlation (ICC) and Kappa method.RESULTS: There was a strongly positive and statistically significant correlation between ONSD measurement using CT scan and MRI (p value <0.001). There was almost perfect agreement between CT scan and MRI in measuring ONSD (ICC=0.987 and Kappa =0.837). Similar agreement was obtained when cases stratified into normal (≤ 5mm) and thickened (> 5mm) ONSD (ICC=0.947 and 0.972 respectively).CONCLUSION: CT scan is a reliable substitute for MRI in measuring ONSD with almost perfect agreement between the two modalities. It might be good practice to include ONSD measurement in the initial evaluation of brain CT scan in any patient with suspected raised ICP.
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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 | 0.025 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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".