Cerebral Lactate Uptake After Half-Molar Sodium Lactate Therapy in Traumatic Brain Injury: A Brief Report
Bibliographic record
Abstract
Exogenous sodium lactate has many advantages after traumatic brain injury, including intracranial pressure control and alternative energetic supply. It remains unclear, however, whether half-molar sodium lactate (HSL) is effectively incorporated in brain metabolism, which we can verify using the arteriovenous difference in lactate (AVD lac ). Hence we compared the AVD lac in patients with severe traumatic brain injury receiving an equiosmolar bolus of sodium lactate or mannitol for intracranial hypertension (IH) treatment. We included 23 patients: 14 received HSL for 25 IH episodes, and nine received mannitol for 19 episodes (total of 44 IH episodes). We observed that the median variation in AVD lac was positive in the group that received HSL (Δ +0.1 [IQR -0.08–0.2] mmol/L), which suggests a net lactate uptake by the brain. On the other hand, it was negative in the group that received mannitol (Δ -0.0 [IQR -0.1 to 0.0] mmol/L), indicating a net lactate export. Finally, there were more positive AVD lac values in the group that received HSL and more negative AVD lac values in the group that received mannitol (Fisher exact p = 0.04). Our study reports the first evidence of a positive AVD lac , which corresponds to a net lactate uptake by the brain, in patients who received HSL for severe TBI. Our results constitute a bedside confirmation of the integration of lactate into the brain metabolism and pave the way for a wider dissemination of sodium lactate in the daily clinical care of patients with traumatic brain injury.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".