Evaluating perioperative glycemic status after different types of pancreatic surgeries via continuous glucose monitoring system: a pilot study
Bibliographic record
Abstract
BACKGROUND: Perioperative glycemic status after pancreatic surgery has never been described. However, it's essential for optimal perioperative glucose management and understanding the pathogenesis of new-onset diabetes mellitus (NODM) after pancreatectomy. Continuous glucose monitoring (CGM) system provides us a helpful tool for closely monitoring and studying perioperative glucose change. This study tried to describe and compare perioperative glucose level and glycemic variability between different types of pancreatic surgeries via CGM device. METHODS: This study was designed as a prospective observational study. Eighteen patients were enrolled and were grouped by different types of surgery received: control group (CTRL), pancreaticoduodenectomy (PD), distal pancreatectomy (DP), and total pancreatectomy (TP). CGM devices were implanted and initiated right after the surgery. Mean glucose value (MGV), coefficient of variation (CV), mean of daily difference (MODD), continuous overall net glycemic action (CONGA), and time above range (TAR)/time below range (TBR) was compared between groups to assess glucose level and glycemic variability. RESULTS: TP showed the highest MGV and CV among all groups (P<0.001), while CTRL showed the lowest (P<0.001). PD and DP had similar MGV and CV lower than TP but higher than CTRL (P<0.001). TP had the highest MODD and CONGA, CTRL had the lowest, but no significant differences were found between groups. TP had the highest TAR (24.29%) and the lowest TBR (1.28%), while the control group showed the opposite. The differences in TAR/TBR between groups were all significant (P<0.05). CONCLUSIONS: TP had the highest mean glucose level and the greatest glycemic variability. PD and DP had similar results: a higher mean glucose level than control but lower than TP. For glycemic variability, PD and DP seemed to have a near-normal result resembling the control group. CGM is useful for glucose monitoring in the perioperative management of pancreatic surgery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".