Does gender influence morbidity or mortality in the surgical treatment of renal masses?
Bibliographic record
Abstract
INTRODUCTION: We determine the relationship between gender and surgical morbidity after radical nephrectomy (RN) and partial nephrectomy (PN) for renal masses on a population level. METHODS: We conducted a population-based, retrospective study using the Canadian Institute for Health Information Discharge Abstract Database. This included 20 286 RNs (82.5%) and 4292 PNs (17.5%) from April 1, 1998 to March 31, 2008. Complications were identified by ICD-9 and 10 codes, and comorbidity was assessed with the Charlson Index. The association between gender and in-hospital complication rates and mortality were examined using the Chi-square test, as well as with multivariable logistic regression, adjusting for explanatory variables including type of surgery, age, and comorbidity. RESULTS: Overall, men experienced a higher unadjusted complication rate than women (35.1% vs. 32.7%), as well as a higher unadjusted in-hospital mortality rate (1.46% vs. 0.84%), respectively. Men also demonstrated significantly higher rates of cardiac, wound, nephrectomy-specific, and medical complications. Women experienced fewer complications than men after RN (p = 0.0002), but not after PN (p = 0.33). On multivariable logistic regression analysis, women had a lower overall complication rate (odds ratio [OR] 0.94, 95% confidence interval [CI] 0.88-0.99), and a lower inhospital mortality rate (OR 0.64, 95% CI 0.49-0.83) after kidney surgery. CONCLUSIONS: In our population-based analysis, in-hospital morbidity after renal surgery was significantly lower for women. Further study is needed to determine if the observed effect is related to differences in surgical difficulty, perioperative care, or unmeasured confounders.
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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 | 0.001 | 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) | 0.002 | 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".