Risk Factors and Prognostic Impact of Postoperative Complications in Patients with Advanced Gastric Cancer Receiving Neoadjuvant Chemotherapy
Bibliographic record
Abstract
Background: Neoadjuvant chemotherapy is important to improve the prognosis of patients with advanced gastric cancer. However, it may result in postoperative complications (POCs). The aim of this study is to evaluate risk factors and prognostic impact of POCs in patients receiving neoadjuvant chemotherapy. Methods: We retrospectively collected clinical information of patients who underwent curative gastrectomy after receiving neoadjuvant chemotherapy between 2011 and 2018. Overall survival (OS) was analyzed using the Kaplan–Meier method. Logistic regression and Fisher’s exact test were used to evaluate risk factors for complications. Results: A total of 176 patients were included in our study. The 3-year OS rates for the complication group (n = 30) and non-complication group (n = 146) were 36.7% and 52.7%, respectively (p = 0.0294). Age, BMI, multivisceral resection and operation time were independent risk factors for POCs in patients. Patients with multivisceral resection were more likely to suffer from grade III-IV complications (p = 0.026). Inflammation complications might occur in patients with high BMI (p = 0.017). Low preoperative albumin seemed to be a risk factor for leakage complications (p = 0.033). Conclusions: Our study revealed that patients with POCs had a poor prognosis and we identified the risk factors for complications so that POCs can be avoided in time.
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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 | 0.003 |
| 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) | 0.001 | 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".