Examining the association between adiposity and biochemical recurrence after radical prostatectomy
Bibliographic record
Abstract
Introduction: Herein, we examined the association between adiposity, as measured by computed tomography (CT), and biochemical recurrence (BCR) after radical prostatectomy (RP).Methods: Using axial CT images, preoperative fat mass index (FMI) was calculated for 698 men who underwent RP from 2007–2010 by using measurements of total surface area of adipose tissue at the L3 level. Obesity was classified according to National Health and Nutrition Examination Survey (NHANES) standards for obesity (FMI >9 kg/m2). The associations between obesity and the distribution of adiposity (visceral vs. subcutaneous) with BCR were examined using the Kaplan-Meier method and Cox proportional hazards regression analyses.Results: Obese men were older than non-obese men (63.0 vs. 60.7 years; p<0.001), but were similar with regards to all other clinical and pathological characteristics. With a median followup of six years, 152 patients were diagnosed with BCR. Five-year BCR-free survival was similar between obese and non-obese patients (80.6% vs. 82.1%; p=0.27). Furthermore, in multivariable analyses, obesity was not independently associated with the risk of BCR (hazard ratio [HR] 1.02; 95% confidence interval [CI] 0.73–1.43). Similar results were obtained when analyzing FMI as a continuous variable (HR 1.02; 95% CI 0.94–1.09 for each 1 kg/m2 increase in FMI). Additionally, neither visceral adiposity, subcutaneous adiposity, or visceral-to-subcutaneous adiposity ratio were associated with BCR (all p>0.05) in multivariable analyses.Conclusions: Neither total abdominal adiposity nor the distribution of adiposity were independently associated with BCR after RP in this study. As such, the presence of obesity may not be a marker of increased oncological risk after RP.
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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 | 0.002 |
| 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) | 0.001 | 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".