Clinical significance of preoperative albumin and alkaline phosphatase in colorectal cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between preoperative serum levels of albumin (ALB) and alkaline phosphatase (ALP) with postoperative outcome in colorectal cancer (CRC) patients. METHODS: A thorough literature search was conducted across Embase, PubMed, and Cochrane Library databases, identifying 20 eligible studies encompassing 61,296 participants. Studies were primarily observational and case-control in nature, with some randomized controlled trials also included. The random effects model was utilized to synthesize the effect sizes, while study quality was appraised using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias Assessment Tool. RESULTS: Findings revealed that CRC patients with preoperative ALB levels below 3.5 g/dl were at an elevated risk for postoperative complications (OR = 2.56, 95% CI: 2.12-3.08), increased mortality (OR = 4.54, 95% CI: 2.02-10.20), and a poorer prognostic survival risk (HR = 2.09, 95% CI: 1.58-2.77). Additionally, elevated ALP levels were associated with a higher risk of poor overall survival (HR = 1.67, 95% CI: 1.44-1.94). However, publication bias was noted in some studies. CONCLUSION: Preoperative hypoalbuminemia and elevated ALP levels are significantly linked to adverse postoperative events and reduced survival in CRC patients, suggesting their potential as prognostic biomarkers.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".