The Effect of Gender, Age and BMI on Postoperative C-Reactive Protein Levels After Major Abdominal Surgery
Bibliographic record
Abstract
Background: The aim of this observational study was to analyze whether differences exist in postoperative C-reactive protein (CRP) levels in elderly, obese or for gender after major abdominal surgery, which might affect the diagnostic value of CRP. Methods: Observational cohort study was made between January 2009 and January 2011 of all adult patients who underwent major abdominal surgery. Medical records and radiology were reviewed. Complications were recorded based on the classification by Clavien-Dindo. CRP data were collected up to 14 days postoperatively, or until discharge. Results: Three hundred ninety-nine patients underwent major abdominal surgery. Seventy-four patients underwent upper gastrointestinal (GI) surgery, 91 patients underwent hepato-pancreatico-billiary (HPB) surgery and 234 underwent lower GI surgery. Two hundred thirty-five patients were male. Eighty-three patients presented with a major complication (20.8%). No effect of gender or age on postoperative CRP levels was observed. Although a positive correlation was observed for BMI, this did not hold in further regression analysis. Conclusions: No effect was observed for gender, age and BMI on postoperative CRP levels. It is proposed that the effect of surgical trauma and inflammation surpasses the effect of these related patient factors. The results further support the use of CRP as an independent marker of postoperative inflammation and complications. J Curr Surg. 2014;4(3):61-69 doi: http://dx.doi.org/10.14740/jcs236w
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".