Effects of Prone Position and Positive End-Expiratory Pressure on Noninvasive Estimators of ICP: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Prone positioning and positive end-expiratory pressure can improve pulmonary gas exchange and respiratory mechanics. However, they may be associated with the development of intracranial hypertension. Intracranial pressure (ICP) can be noninvasively estimated from the sonographic measurement of the optic nerve sheath diameter (ONSD) and from the transcranial Doppler analysis of the pulsatility (ICPPI) and the diastolic component (ICPFVd) of the velocity waveform. METHODS: The effect of the prone positioning and positive end-expiratory pressure on ONSD, ICPFVd, and ICPPI was assessed in a prospective study of 30 patients undergoing spine surgery. One-way repeated measures analysis of variance, fixed-effect multivariate regression models, and receiver operating characteristic analyses were used to analyze numerical data. RESULTS: The mean values of ONSD, ICPFVd, and ICPPI significantly increased after change from supine to prone position. Receiver operating characteristic analyses demonstrated that, among the noninvasive methods, the mean ONSD measure had the greatest area under the curve signifying it is the most effective in distinguishing a hypothetical change in ICP between supine and prone positioning (0.86±0.034 [0.79 to 0.92]). A cutoff of 0.43 cm was found to be a best separator of ONSD value between supine and prone with a specificity of 75.0 and a sensitivity of 86.7. CONCLUSIONS: Noninvasive ICP estimation may be useful in patients at risk of developing intracranial hypertension who require prone positioning.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".