Effect of patient end-tidal carbon dioxide levels on cerebral regional oxygen saturation and inflammatory cytokine concentrations in elderly patients undergoing laparoscopic radical gastrectomy: a randomized controlled trial
Bibliographic record
Abstract
Carbon dioxide levels can affect inflammation and cerebral regional oxygen saturation (rScO2), a pathophysiological indicator of early postoperative cognitive dysfunction (POCD).To compare the cerebral rScO2 and postoperative inflammation based on patient end-tidal carbon dioxide (PET CO2) levels in elderly patients undergoing laparoscopic radical gastrectomy. Ninety patients were randomly divided into three groups according to PET CO2 level: group L (low PET CO2, 36–40 mmHg), group C (control PET CO2, 41–45 mmHg) and group H (high PET CO2, 46–50 mmHg). Eighty-four patients (age, range: 60–80 years) scheduled to undergo radical laparoscopic gastrectomy were included in the final analysis. The indicated PET CO2 was maintained for patients in each group. Their intraoperative cerebral rScO2 was recorded at indicated timepoints (T0–T6) from arrival at the operating room to post-extubation. The Montreal Cognitive Assessment Test (MoCA) and changes in serum concentrations of inflammatory cytokines were measured at 1 h, 1d and 7 d after surgery. The cerebral rScO2 at T3 (1 h post-skin incision)–T5 (completion of skin sutures) was significantly higher in group H than in group L, while the MoCA score was not significantly different between group H and L (p > 0.05). At 1 h, 1 d and 7 d after surgery, the concentrations of inflammatory cytokines, including interleukin 6 (IL-6) and S100β, were significantly higher in group L than in group H (p < 0.05). At 1 h and 7 d after surgery, the inflammatory cytokines were significantly higher in group C than in group H. Our findings suggest that higher PET CO2 levels increased cerebral rScO2 and reduced the serum concentrations of IL-6 and S100β but may not affect the MoCA score in elderly patients after radical laparoscopic gastrectomy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".