Adverse in‐hospital outcomes in patients with paraplegia who undergo radical prostatectomy
Bibliographic record
Abstract
OBJECTIVE: To test for the association between paraplegia and perioperative complications as well as in-hospital mortality after radical prostatectomy (RP) for non-metastatic prostate cancer. PATIENTS AND METHODS: We identified patients who underwent RP (National Inpatient Sample [NIS] 2000-2019), stratified according to paraplegia status. The NIS is an inpatient database that rests on data contributed by ~20% of community hospitals within the United States. Descriptive analyses, propensity score matching (PSM, ratio 1:10), and multivariable logistic regression models (LRMs) were used. RESULTS: Of 260 302 patients who underwent RP, there were 223 (0.1%) with paraplegia. The patients with paraplegia who underwent RP were younger (age 60 vs 62 years; P = 0.002) and more frequently had Charlson Comorbidity Index ≥3 (46% vs 2.2%; P < 0.001). After 1:10 PSM, 223/223 (100%) patients with paraplegia and 2230/260 079 (0.9%) without paraplegia who underwent RP were included in further analyses. In multivariable LRMs, patients with paraplegia who underwent RP exhibited significantly higher in-hospital mortality (adjusted odds ratio [aOR] 10.7), higher rates of wound complications (aOR 8.2), infectious complications (aOR 6.2), genitourinary complications (aOR 3.5), intraoperative complications (aOR 2.8), cardiac complications (aOR 2.8), pulmonary complications (aOR 2.6), overall complications (aOR 2.4), blood transfusions (aOR 1.8), and longer length of stay ≥75th percentile (aOR 1.7) (all P ≤ 0.01). CONCLUSION: Although patients with paraplegia who undergo RP are rare, adverse in-hospital outcomes are substantially more frequent in these individuals. These observations should be carefully considered in clinical decision making and informed consent prior to RP, if such procedure is contemplated in patients with paraplegia.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".