The impact of fellowship training on pathological outcomes following radical prostatectomy: a population based analysis
Bibliographic record
Abstract
BACKGROUND: Radical prostatectomy (RP) is a common treatment for prostate cancer (PCa). Morbidity, mortality and pathological outcomes may be superior in academic institutions. One explanation may be the involvement of oncology fellowship trained urologists within academic institutions. The literature examining pathological outcomes often lacks individual surgeon data. The objective of this study was to compare pathological outcomes following RP between fellowship trained and non-fellowship trained urologists. METHODS: Population-based, retrospective chart review of men diagnosed with PCa between 2003 and 2008, the majority treated with open approach RP (>99%). Pathological outcomes were compared between oncology fellowship trained academic (FTA), non-fellowship trained academic (NFTA) and non-academic (NA) urologists. Relationships with pathological outcomes were examined utilizing multivariable logistic regression. RESULTS: 83.1% of eligible patients were included in our analysis resulting in 1075 patients. In multivariable analysis, surgeon group was an independent predictor of positive surgical margin (PSM) (p < 0.0001). NFTA and NA urologists were more likely to have PSM compared to FTA urologists (OR 2.50; 95% CI: 1.44-4.35 and OR 2.10; 95% CI: 1.53-2.88, respectively). However, the proportion of PSM between NFTA and NA urologists was not significant (p = 0.492). In addition, pathological stage (p = 0.0004), Gleason sum (p < 0.0001), and surgeon volume (p = 0.017) were associated with PSM. Limitations include retrospective design and lack of clinical and functional outcomes. CONCLUSIONS: Uro-oncology fellowship trained surgeons had significantly lower rates of PSM than non-fellowship trained surgeons in this population based cohort. This study demonstrates the importance of surgeon-related variables on pathological outcomes and highlights the value of additional urologic oncology fellowship training.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".