Risk Factors for Postoperative Nausea and Vomiting After TACE: A Prospective Cohort Study
Bibliographic record
Abstract
Objective: Postoperative nausea and vomiting (PONV) was one of the common complications in patients with HCC who had undergone TACE. This study was a prospective analysis of patient data to investigate risk factors for PONV in patients after TACE. Material and Methods: Data were collected from 212 patients undergoing TACE in the interventional department between August 2022 and August 2023. Including: gender, age, education, BMI, operation time, concomitant underlying diseases and drugs, preoperative limosis, history of nausea and vomiting, history of kinetosis, history of smoking or drinking, and occurrence of PONV. A visual analog scale was used to measured pain. Neuropsychological status was also assessed, using the 7-item Generalized Anxiety Disorder Questionnaire (GAD-7) and the Patient Health Questionnaire-9(PHQ-9). To identify risk factors for PONV, multiple logistic regression analysis was used. The receiver operating characteristic (ROC) curve was plotted to assess the regression model. The clinical trial number did not apply in the study. Results: In this study, 212 out of a total of 904 patients with HCC undergoing TACE during their hospital stay were included for analysis. Among the included patients, the incidence of PONV was as high as 42% (89/212). Multiple logistic regression analysis showed that chronic gastritis (odds ratio [OR] = 10.350; p = 0.020), VAS (OR = 3.835; p = 0.003), epirubicin (OR = 26.685; p < 0.001), and the dosage of lipiodol (≥5 mL) (OR = 1.385; p < 0.001) were independent risk factors of PONV after TACE. The ROC curve demonstrated that the AUC was 0.902, the sensitivity was 84.3%, and the specificity was 87%. Conclusions: PONV is highly prevalent among patients with HCC after TACE. Chronic gastritis, pain, epirubicin, and the dosage of lipiodol were independent risk factors for PONV. The risk prediction model that was constructed according to the aforementioned factors demonstrated good discriminatory capacity for predicting the risk of post-TACE PONV, which can improve the recognition of medical providers, and has a good ability to prevent and treat nausea and vomiting.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".