The efficacy and safety of glucocorticoid for perioperative patients with hepatectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
Glucocorticoids have been used in patients undergoing perioperative hepatectomy, however their safety and efficacy remain controversial. This meta-analysis was conducted to investigate this issue and further provide reference for clinical practice. PubMed/MEDLINE, Embase, and Cochrane Library were searched for randomized controlled trials (RCTs) from database inception to December 2022. Literature screening and data extraction were performed independently by two reviewers. The methodological quality of the RCTs was assessed using the Jadad scale. RevMan 5.4 was used for the meta-analysis. A total of 11 RCTs involving 905 patients were included. Compared with the control group, we found perioperative glucocorticoid administration significantly lowered overall complication rate [RR = 0.67; 95% CI (0.55, 0.83); P = 0.0003], infectious complication rate [RR = 0.41; 95% CI (0.21, 0.82); P = 0.01] and postoperative liver failure [RR = 0.63; 95% CI (0.41, 0.97); P = 0.03]. In addition, glucocorticoids appear to improve liver function (TBil) [MD = −0.36, 95% CI (−0.59, −0.14), P = 0.001] and reduce the release of certain inflammatory cytokines (IL-6) [MD = −48.52, 95% CI (−56.88, −40.16), P < 0.00001]. Based on the available evidence, glucocorticoids appear to be safe and effective in patients undergoing hepatectomy, but further research is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".