Cirrhosis and liver disease vs. adverse in-hospital outcomes after radical prostatectomy
Bibliographic record
Abstract
INTRODUCTION: Radical prostatectomy (RP) may be a treatment option for prostate cancer patients with cirrhosis and liver disease (CLD). However, the effect of CLD on adverse in-hospital outcomes after RP has not been well described. METHODS: Descriptive analyses, propensity score matching (PSM), and multivariable logistic and Poisson regression models were used to address National Inpatient Sample RP patients between 2005 and 2019. CLD severity was stratified as mild vs. moderate/severe. RESULTS: Of 191,050 RP patients, 1,559 (0.8%) had CLD. Of those, 1,515 (97.2%) vs. 44 (2.8%) were classified as having mild and moderate/severe CLD, respectively. Any CLD rate increased from 0.6% to 1.5% (2005-2019, EAPC: +7.9%, P < 0.001). CLD patients exhibited higher rates of all 15 examined adverse in-hospital outcomes. The absolute differences were largest for overall complications (+13.9%), length of stay >2 days (+8.9%), and blood transfusions (+4.0%, all P < 0.001). After detailed multivariable adjustment, CLD independently predicted higher rates of all 15 adverse in-hospital outcomes (P < 0.01). The detrimental effect was most pronounced for in-hospital mortality (multivariable odds ratio (OR) 8.74), infectious complications (OR 4.59), and hepatic complications (OR 4.45). Finally, a convincing dose-response relationship, where the effect magnitude of moderate/severe CLD was at least 3 times higher than that of mild CLD, applied in 4 of 15 comparisons. CONCLUSIONS: CLD patients exhibited higher rates of adverse in-hospital outcomes after RP. However, mild CLD did not exert a prohibitive effect that would clearly preclude RP as a treatment option.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".