Association Between Discharge Medications and Oncologic Post‐Embolization‐Syndrome‐Related Outcomes
Bibliographic record
Abstract
BACKGROUND: Post-embolization syndrome after transarterial chemoembolization (TACE) and Yttrium-90 radioembolization (TARE) causes significant morbidity. Understanding whether discharge prescriptions influence short-term outcomes may guide standardized pain-management strategies. METHODS: A retrospective cohort study of 3191 patients (3988 procedures) with hepatocellular carcinoma from the Merative MarketScan Databases (2009-2022) was performed. The composite outcome was 7-day drug escalation or hospital readmission. Bivariate logistic regression identified candidate variables (p < 0.10); multivariable logistic regression with patient-clustered robust standard errors estimated adjusted odds ratios (aORs), adjusting for age, sex, and Charlson Comorbidity Index (CCI). RESULTS: Compared to patients discharged without opioids post-chemoembolization, those prescribed opioids at discharge had 83% lower odds of experiencing drug escalation or readmission (odds ratio [aOR] = 0.17, p < 0.001), and those undergoing radioembolization had 59% lower odds (aOR = 0.41, p < 0.001). Being prescribed antiemetics or steroids was also associated with lower odds of escalation/readmission events, with percentages varying by procedure type. CONCLUSIONS: Prescribing opioids, along with antiemetics and steroids, at discharge may reduce the likelihood of post-procedural events, such as drug escalation and readmission, in patients undergoing trans-arterial chemoembolization and radioembolization for hepatocellular carcinoma. These findings highlight the importance of a comprehensive pain management strategy in interventional oncology and warrant consideration in clinical practice guidelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".