Optic Nerve Sheath Diameter Ultrasound for Raised Intracranial Pressure
Bibliographic record
Abstract
Optic nerve sheath diameter (ONSD) ultrasound is becoming increasingly more popular for estimating raised intracranial pressure (ICP). We performed a systematic review and analysis of the diagnostic accuracy of ONSD when compared to the standard invasive ICP measurement. METHOD: We performed a systematic search of PUBMED and EMBASE for studies including adult patients with suspected elevated ICP and comparing sonographic ONSD measurement to a standard invasive method. Quality of studies was assessed using the QUADAS-2 tool by two independent authors. We used a bivariate model of random effects to summarize pooled sensitivity, specificity, and diagnostic odds ratio (DOR). Heterogeneity was investigated by meta-regression and sub-group analyses. RESULTS: We included 18 prospective studies (16 studies including 619 patients for primary outcome). Only one study was of low quality, and there was no apparent publication bias. Pooled sensitivity was 0.9 [95% confidence intervals (CI): 0.85-0.94], specificity was 0.85 (95% CI: 0.8-0.89), and DOR was 46.7 (95% CI: 26.2-83.2) with partial evidence of heterogeneity. The Area-Under-the-Curve of the summary Receiver-Operator-Curve was 0.93 (95% CI: 0.91-0.95, P < .05). No covariates were significant in the meta-regression. Subgroup analysis of severe traumatic brain injury and parenchymal ICP found no heterogeneity. ICP and ONSD had a correlation coefficient of 0.7 (95% CI: 0.63-0.76, P < .05). CONCLUSION: ONSD is a useful adjunct in ICP evaluation but is currently not a replacement for invasive methods where they are feasible.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".