Identifying a Genetic Signature that Predicts the Progression of Non-Muscle Invasive Urothelial Carcinoma to Muscle-Invasive Cancer.
Bibliographic record
Abstract
Bladder cancer (BC) is Canada's fifth most commonly diagnosed cancer, with two distinct types: non-muscle invasive (NMIBC) and muscle-invasive (MIBC). The objectives of this study are to find molecular biomarkers that lead to the progression of MIBC from NMIBC to provide a targeted treatment approach therefore, also using early detection to decrease cases of MIBC and to predict the biomarkers which aid in the transition of high-grade NMIBC to MIBC. The hypothesis states that if molecular biomarkers are identified and predict the progression of MIBC from NMIBC, they can be implemented for clinical use. This study divided 22 BC patients from the Windsor Regional Hospital into two cohorts. The first cohort included NMIBC samples; the second included MIBC samples. Three STAR patient samples began with NMBIC diagnosis and progressed to MIBC during this study. Data sequencing and analysis were conducted to identify sequencing depth, allele frequency and non-synonymous mutations. The results indicated a higher allele frequency and mutational change in MIBC samples. Cell line studies were also conducted, showing increased proliferation rates. Retrospective data was collected from patients’ charts, indicating that 100% of MIBC patients' deaths were related to bladder cancer. This ongoing study brings significant value to the oncology and translational health field.
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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 | 0.001 |
| 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 | 0.000 | 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".