Bibliographic record
Abstract
OBJECTIVE: To assess the value of preoperative albumin to globulin ratio for predicting pathologic and oncological outcomes in patients with upper tract urothelial carcinoma treated with radical nephroureterectomy in a large multi-institutional cohort. MATERIALS AND METHODS: Preoperative albumin to globulin ratio was assessed in a multi-institutional cohort of 2492 patients. Logistic regression analyses were performed to assess the association of the albumin to globulin ratio with pathologic features. Cox proportional hazards regression models were performed for survival endpoints. RESULTS: The optimal cut-off value was determined to be 1.4 according to a receiver operating curve analysis. Lower albumin to globulin ratios were observed in 797 patients (33.6%) compared with other patients. In a preoperative model, low preoperative albumin to globulin ratio was independently associated with nonorgan-confined diseases (odds ratio 1.32, P = 0.002). Patients with low albumin to globulin ratios had worse recurrence-free survival (P < 0.001), cancer-specific survival (P = 0.001) and overall survival (P = 0.020) in univariable and multivariable analyses after adjusting for the effect of standard preoperative prognostic factors (recurrence-free survival: hazard ratio (HR) 1.31, P = 0.001; cancer-specific survival: HR 1.31, P = 0.002 and overall survival: HR 1.18, P = 0.024). CONCLUSIONS: Lower preoperative albumin to globulin ratio is associated with locally advanced disease and worse clinical outcomes in patients treated with radical nephroureterectomy for upper tract urothelial carcinoma. As it is difficult to stage disease entity, low preoperative serum albumin to globulin ratio may help identify those most likely to benefit from intensified care, such as perioperative systemic therapy, and the extent and type of surgery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.726 | 0.618 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".