The impact of diabetes on postoperative infections in colorectal cancer: A meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: This study aims to analyze and summarize the evidence concerning the relationship between diabetes and postoperative infections in colon cancer through a literature review. Methods: A comprehensive search was conducted on Chinese databases, including CNKI, VIP, Wanfang, and the biomedical literature database, as well as the English database PubMed. The search covered the period from February 1, 2003, to February 28, 2023. The Newcastle-Ottawa Scale was employed to score the included literature, and funnel plots along with Egger’s regression test were used to analyze publication bias. Stata 12.0 was utilized for the analysis of the collected raw data. Results: Following inclusion and exclusion criteria, this study incorporated seven retrospective studies, with a total of 4607 cases in the infection group and 9102 cases in the non-infection group. The quality scores of the seven studies ranged between 7 and 8 points. Funnel plot and Egger’s regression test analyses revealed no significant publication bias in the included literature. A correlation was identified between diabetes and postoperative infections in colon cancer, implicating diabetes as a risk factor for such infections. Subgroup analysis indicated that nationality, surgical methods, and infection types had no significant impact on the meta-analysis results. Conclusion: The analysis revealed a significant correlation between diabetes and postoperative infections in colon cancer. Diabetes emerged as a risk factor for postoperative infections, with odds ratios (OR) of 3.82 (P＞0.1) and 95% CI of 2.91-5.01. Controlling blood glucose levels was associated with a reduced risk of postoperative infections in colon cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it