Body mass index as a risk factor for increased serum lactate during craniotomy.
Bibliographic record
Abstract
BACKGROUND: An increase in serum lactate can occur in patients undergoing craniotomy. We hypothesized that prolonged craniotomy for brain tumor resection leads to inadequate tissue perfusion as demonstrated by increased level of lactate. This study attempts to determine the mechanism and identify any modifiable risk factors. METHODS. Prospective, observational study of 18 patients undergoing craniotomy for brain tumor resection. The primary outcome was that peak serum lactate would correlate with length of surgery. Secondary outcomes included lactate at 3, 6 and 9 hours, creatine kinase (CK) and myoglobinuria overtime. These values were correlated with expected risk factors for lactatemia including length of surgery, Body Mass Index (BMI), hypotension, hemoglobin and mannitol therapy. RESULTS. Serum lactate consistently increased in the first 3 hours in all patients (2.21±1.22 mmol/L) with a peak increase at 9 hours (3.73±1.62 mmol/L) (P<0.05 for both). The peak serum lactate did not correlate with length of surgery (P=0.799). However, the change in lactate over 3 hours (Δ3hrLactate) did correlate with BMI (P=0.010). Serum CK was increased at 12 hours (P<0.05) and reached a peak level greater than 1000 U/L in 8 of 18 patients. Six of these patients experienced myoglobinuria. No other parameters correlated with increased lactate. CONCLUSION: We observed a consistent and early increase in serum lactate in patients undergoing craniotomy, which correlated with BMI, but not length of surgery. Associated increases in CK and myoglobinuria support the hypothesis that elevated BMI contributed to muscle ischemia and tissue breakdown during craniotomy. Future studies are required to establish the overall clinical significance and mechanism of hyperlactatemia during neurosurgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".