Effect of craniotomy on oxidative stress and its effect on plasma <scp>l</scp>-carnitine levels
Bibliographic record
Abstract
OBJECTIVE: to investigate the impact of craniotomy on oxidative stress and its effect on levels of plasma L-carnitine (LC). METHODS: plasma levels of reactive oxygen species, superoxide dismutase (SOD), glutathion peroxidase (GSH-Px), catalase (CAT), total antioxidative capacity (T-AOC), and thiobarbituric acid reactive substances (TBARS) were measured in 34 patients (26 males and 8 females, mean age 47.7 ± 6.7 years) before and after craniotomy. Plasma levels of LC, acetyl-L-carnitine (ALC), and propionyl-L-carnitine (PLC) were also measured before and after the craniotomy. RESULTS: the plasma concentrations of SOD, GSH-Px, CAT, and T-AOC within the first 4 h after craniotomy were lower than their baseline values (P < 0.05). There were no statistically significant differences in the mean plasma levels of SOD, GSH-Px, CAT, or T-AOC between the baseline and 24 h post-operative values. The level of TBARS at 4 h after the craniotomy was lower than the pre-operative level (P < 0.05), but the 24 h post-operative value was similar to the baseline concentration (P > 0.05). Plasma levels of LC, ALC, and PLC were lower after the craniotomy (P < 0.05), but these levels returned to the baseline levels 24 h after the operation. CONCLUSIONS: craniotomy and the associated procedures for surgery/anesthesia temporarily reduce antioxidant activity and plasma levels of L-carnitine.
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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 | 0.000 |
| 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) | 0.002 | 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".