Effect of Neoadjuvant Chemotherapy on Renal Function following Radical Cystectomy: Is there a Meaningful Impact?
Bibliographic record
Abstract
Objective: To evaluate the patterns of impact of neoadjuvant chemotherapy (NAC) on renal function across the initial year following treatment for muscle-invasive bladder cancer (MIBC) with radical cystectomy (RC).Methods: We reviewed the charts of 241 patients who underwent RC for urothelial carcinoma of the bladder between 2003-14 at our institution.Renal function was evaluated at multiple time points (pre-chemotherapy, pre-operatively, post-operatively, 6-12 months follow-up), and then classified by CKD staging.Univariable and multivariable logistic regression analyses were performed to determine relationship between NAC and change in CKD stage.Results: Of the 241 patients who underwent RC for urothelial carcinoma of the bladder, 66 (27%) received NAC and 175 (73%) did not.In multivariable analysis, NAC was significantly associated with a decrease of at least one CKD stage from baseline to post-op (p = 0.009), but not to the 6-12 months follow-up time point (p = 0.050).The loss of GFR in the NAC cohort occurs up-front with chemotherapy, but the peri-operative course is similar to those who underwent cystectomy alone.Of the 15 NAC patients (26.8%) who were Stage 3 CKD prior to chemotherapy, none progressed to a higher stage CKD.Conclusion: NAC is associated with an initial decline in GFR, which then remains stable through the first year following RC.Despite an initial insult, patients receiving NAC are not vulnerable to further deterioration.When appropriately selected, NAC does not appear to result in a clinically significant deterioration of renal function.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".