Hyperglycemia and Brain Tissue pH after Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: Hyperglycemia occurring after head injury is associated with poor neurological outcome. We tested the hypothesis that blood glucose levels are associated with brain tissue pH (pH(b)) and that the correction of hyperglycemia would result in an improvement in pH(b). METHODS: This is a retrospective analysis of a prospectively collected database. Thirty-four patients in a tertiary care neuroscience critical care unit with major traumatic brain injury underwent pH(b) monitoring. RESULTS: A total of 428 glucose measurements were recorded during pH(b) monitoring. Mean glucose level was 7.1 mmol/L (range, 2.8-21.7 mmol/L) and median (interquartile range) pH(b) was 7.11 mmol/L (7.00-7.19 mmol/L). To account for the correlated, unbalanced nature of the data, a linear generalized estimating equation model was created. This model predicted that for each 1 mmol/L increase in blood glucose, pH(b) changed by -0.011 mmol/L (95% confidence interval, -0.016 to -0.005 mmol/L; P < 0.001). This relationship remained significant in a multivariable model that included cerebral perfusion pressure, brain tissue oxygen and carbon dioxide tension, and brain temperature. Twenty-one episodes of significant hyperglycemia (>or=11.1 mmol/L) treated with intravenous insulin were identified. Insulin therapy significantly reduced blood glucose concentration from a median (interquartile range) of 11.9 mmol/L (range, 11.4-13.6 mmol/L) to 8.8 mmol/L (range, 7.3-9.6 mmol/L; P < 0.001). Baseline pH(b) was not significantly different from pH(b) associated with the subsequent glucose reading of less than 11.1 mmol/L (P = 0.29), but there was a suggestion of improvement if the change in blood glucose was large. CONCLUSION: Blood glucose is associated with brain tissue acidosis in patients with major head injury. Prospective studies are required to confirm these results and to determine whether treatment of hyperglycemia improves outcome.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".