Serum lactate as a potential biomarker of meningioma malignancy and preoperative treatment effect
Bibliographic record
Abstract
Introduction: Serum lactate levels are useful indicators of illness severity such as sepsis. Previous investigations have shown that lactate is a potential biomarker for glioma malignancy; mechanism of which may be related to Warburg effect - accelerated lactate production when tumors uniquely undergo aerobic glycolysis. Our study reveals a correlation between serum lactate and meningioma WHO grade. We also observed a relationship between radiation effect on metastatic brain tumors and lactate levels. Methods: Data was collected from the charts of 14 patients with grade I meningiomas, 6 grade II meningiomas, and 9 metastatic brain tumors who underwent resection at our institution from 2013-2014. T test and ANCOVA were carried using R software controlling for base deficit. Results: The mean age was 53 years, with 75% females. There was a statistically significant change in intra- and post-operative lactate during meningioma resections, which had a strong positive correlation with grade (p<0.005). Interestingly, the lactate rise was not significant for metastatic brain tumors (p=0.13), but had a positive correlation with tumors that received pre-operative radiation (p<0.05). Conclusion: Lactate is a potential non-invasive biomarker for brain tumor malignancy, as demonstrated in gliomas and meningiomas. Identifying metabolic biomarkers and their relationship to tumor pathology is important to understanding disease processes and improving patient care.
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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 | 0.003 |
| 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 | 0.000 |
| 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".