Tumor Markers as Predictors of Acute Kidney Injury Incidence and Staging of the Muscle-Invasive Bladder Cancer Receiving Chemoradiation Therapy
Bibliographic record
Abstract
Background: Bladder cancer, as one of types of cancers within the urinary tract, is associated with a greater risk of acute kidney injury (AKI), resulting in a poorer prognosis, discontinuation of effective oncological treatments, longer hospitalization, and higher expenses. There is no discussion yet on tumor markers in bladder cancer. With the revolutionary advances in bladder cancer molecular subtyping over the past decade, the presence of tumor markers to assess the staging of bladder cancer has yet to be discussed. In this study, we intended to assess the relationship between tumor markers and incidence of AKI, also between tumor markers and the cancer staging. Methods: This retrospective cross-sectional study utilized secondary data from 26 medical records of patients diagnosed with bladder cancer at the Adam Malik and Universitas Sumatera Utara Hospital between 2021and 2022. This study included all patients with bladder cancer who met the inclusion criteria. Continuous variables were reported as mean (standard deviation (SD)) and examined using an independent t -test. Categorical variables were reported as proportions, examined using Chi-square or Fisher’s exact test. Pre- and post-tumor marker data were evaluated with dependent sample t -test for normal variance data, and Wilcoxon test for data with atypical distribution. P values were set at 0.05. Results: CD44 (P = 0.003) and programmed cell death 1 (PD-1) (P = 0.030) were the only significant markers in their pre- and post-chemoradiation states among the four investigated tumor markers in this study. Meanwhile, PD-1 tumor marker levels were only found to be significant between AKI and pre-chemoradiation (P = 0.011). Even though the multivariate study of tumor staging did not show any statistical significance, both tumor markers CD44 and PD-1 showed a significant effect on the incidence of acute renal damage (P = 0.034). Conclusions: Pre-chemoradiation PD-1 tumor markers showed promise as good predictive indicators for staging and AKI incidence in muscle-invasive bladder cancer patients undergoing chemoradiation therapy. World J Oncol. 2023;14(5):423-429 doi: https://doi.org/10.14740/wjon1676
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".