Can We Predict a Higher Risk of Urothelial Bladder Cancer With a Simple Blood Test?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND/AIM: The COVID-19 pandemic highlighted the need to develop tools prioritizing high risk patients for urgent evaluation. Our objective was to determine whether Glasgow Prognostic Score (GPS), an inflammation-based score, can predict higher grade and stage urothelial bladder cancer in patients with gross hematuria who need urgent evaluation. PATIENTS AND METHODS: We analyzed a database of 129 consecutive patients presenting with gross hematuria. GPS was calculated using pretreatment C-reactive protein (CRP) and albumin levels. Patients with bacteriuria or other known malignancies were excluded. The relationship between GPS and final diagnosis was analyzed with multivariate logistic regression. RESULTS: A total of 101 patients were included in the study and 24 patients were identified without any pathology and 77 with a bladder tumor. Pathology demonstrated 21 with muscle invasive, 18 with high grade non-muscle invasive, and 38 with low grade superficial bladder cancer. Twenty-six of 39 (67%) patients with high grade tumors had a GPS of 1 or 2 compared to only 8 out of 62 (13%) patients with either low grade or negative findings (p<0.0001). Ten of 21 (48%) patients with muscle invasive disease had a GPS of 2 compared to 1 out of 18 (6%) with high grade non muscle invasive tumors (p=0.04). On multivariate analysis, GPS was a strong independent predictor of high grade and stage bladder cancer. CONCLUSION: GPS may serve as a highly accessible predictor of high grade, high stage, and large urothelial bladder tumors at the time of initial evaluation and can help identify patients who need urgent evaluation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.008 | 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 it