Influence of mild-moderate hypocapnia on intracranial pressure slow waves activity in TBI
Bibliographic record
Abstract
BACKGROUND: In traumatic brain injury (TBI) the patterns of intracranial pressure (ICP) waveforms may reflect pathological processes that ultimately lead to unfavorable outcome. In particular, ICP slow waves (sw) (0.005-0.05 Hz) magnitude and complexity have been shown to have positive association with favorable outcome. Mild-moderate hypocapnia is currently used for short periods to treat critical elevations in ICP. Our goals were to assess changes in the ICP sw activity occurring following sudden onset of mild-moderate hypocapnia and to examine the relationship between changes in ICP sw activity and other physiological variables during the hypocapnic challenge. METHODS: -reactivity test. The time series were first treated using FFT filter (pass-band set to 0.005-0.05 Hz). Power spectral density analysis was performed. We calculated the following: mean value, standard deviation, variance and coefficient of variation in the time domain; total power and frequency centroid in the frequency domain; cerebrospinal compliance (Ci) and compensatory reserve index (RAP). RESULTS: Hypocapnia led to a decrease in power and increase in frequency centroid and entropy of slow waves in ICP and FV (not ABP). In a multiple linear regression model, RAP at the baseline was the strongest predictor for the decrease in the power of ICP slow waves (p < 0.001). CONCLUSION: In severe TBI patients, a sudden mild-moderate hypocapnia induces a decrease in mean ICP and FV, but also in slow waves power of both signals. At the same time, it increases their higher frequency content and their morphological complexity. The difference in power of the ICP slow waves between the baseline and the hypocapnia period depends on the baseline cerebrospinal compensatory reserve as measured by RAP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".