Pre-therapy serum albumin-to-globulin ratio in patients treated with neoadjuvant chemotherapy and radical nephroureterectomy for upper tract urothelial carcinoma
Bibliographic record
Abstract
The accurate selection of patients who are most likely to benefit from neoadjuvant chemotherapy is an important challenge in oncology. Serum AGR has been found to be associated with oncological outcomes in various malignancies. We assessed the association of pre-therapy serum albumin-to-globulin ratio (AGR) with pathologic response and oncological outcomes in patients treated with neoadjuvant platin-based chemotherapy followed by radical nephroureterectomy (RNU) for clinically non-metastatic UTUC.We retrospectively included all clinically non-metastatic patients from a multicentric database who had neoadjuvant platin-based chemotherapy and RNU for UTUC. After assessing the pretreatment AGR cut-off value, we found 1.42 to have the maximum Youden index value. The overall population was therefore divided into two AGR groups using this cut-off (low, < 1.42 vs high, ≥ 1.42). A logistic regression was performed to measure the association with pathologic response after NAC. Univariable and multivariable Cox regression analyses tested the association of AGR with OS and RFS.Of 172 patients, 58 (34%) patients had an AGR < 1.42. Median follow-up was 26 (IQR 11-56) months. In logistic regression, low AGR was not associated with pathologic response. On univariable analyses, pre-therapy serum AGR was neither associated with OS HR 1.15 (95% CI 0.77-1.74; p = 0.47) nor RFS HR 1.48 (95% CI 0.98-1.22; p = 0.06). These results remained true regardless of the response to NAC.Pre-therapy low serum AGR before NAC followed by RNU for clinically high-risk UTUC was not associated with pathological response or long-term oncological outcomes. Biomarkers that can complement clinical factors in UTUC are needed as clinical staging and risk stratification are still suboptimal leading to both over and under treatment despite the availability of effective therapies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".