Macroscopic, but not microscopic, perivesical fat invasion at radical cystectomy is an adverse predictor of recurrence and survival
Bibliographic record
Abstract
OBJECTIVE: To examine whether the presence of microscopic (pT3a) or macroscopic (pT3b) disease worsens the prognosis relative to pT2 disease at radical cystectomy, as the prognostic significance of pT3a vs pT3b perivesical fat invasion (pT3) is controversial. PATIENTS AND METHODS: In all, 242 patients with pT3 disease (pT3a in 88, pT3b in 121) had radical cystectomy and bilateral pelvic lymphadenectomy for transitional cell carcinoma of the urinary bladder; they were compared with 172 who had organ-confined muscle-invasive disease (pT2). For the analyses we used univariable and multivariable Cox regression models of recurrence and cancer-specific survival, adjusted for age, tumour grade, lymphovascular invasion and the presence of lymph node metastases. RESULTS: In multivariable analyses, microscopic perivesical fat extension (pT3a) was not associated with higher recurrence (P = 0.3) or the mortality rate (P = 0.06) vs pT2 disease. Conversely, the presence of deep perivesical fat extension (pT3b) was associated with 1.8 times the rate of recurrence (P = 0.002) and with twice the rate of death (P = 0.001) vs pT2 disease. CONCLUSION: These findings imply that a detailed assessment of the cystectomy specimen for the presence of microscopic perivesical fat invasion might not be necessary, as the presence of pT3a disease has no strong effect on recurrence or mortality. Moreover, patients with pT3a disease might not require more aggressive therapy than their counterparts with pT2 disease. However, further validation of our data is required.
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 | 0.000 | 0.003 |
| 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".