Associations of obesity, abdominal neoplasms and comorbidities
Bibliographic record
Abstract
Background. Multiple recent studies suggest that at least 5–10 % of all malignancies are attributed to metabolic disorders and obesity. Excessive weight may also significantly influence outcomes and aggravate treatment-related adverse effects and patients’ follow-up. The purpose of the study was to clarify the clinical and pathogenetic significance of excessive body weight and comorbidities in patients with abdominal malignant neoplasms. Materials and methods. This cohort-based observational research involves a total of 952 patients (mean age 62.35 ± 11.57 years) with abdominal malignancies divided into study (23.95 %, body mass index ≥ 26 kg/m2) and control (76.05 %, body mass index ≤ 25 kg/m2) groups. The diagnosis, staging, prevalence of the process, and concomitant pathology, several anthropometric and statistical parameters were determined, and treatment outcomes (duration of the postoperative period, incidence of postoperative complications) were assessed. Results. No significant differences in the staging of abdominal malignancies were identified between study and control groups. Relative fat mass was significantly higher in study group compared to controls — 33.56 ± 1.01 % vs. 27.01 ± 2.25 %, p = 0.009. Edmonton Obesity Staging System showed significantly higher stages for study group as well. The mean Charlson comorbidity index in the study group was significantly higher (p = 0.005), especially in male population. Obesity as a factor influencing the incidence of postoperative complications showed prevalence of 0.66 (95% confidence interval (CI) 0.51–0.78), sensitivity of 0.88 (95% CI 0.71–0.96), and specificity of 0.65 (95% CI 0.39–0.85). Conclusions. Excessive body weight is significant factor aggravating condition of patients with abdominal malignancies, increasing the risk of postoperative complications by 1.29–6.96 times.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".