Comparison of Cold-Cup Biopsy Versus Resection Biopsy in the Early Detection of Detrusor Muscle Invasion in the Case of Bladder Tumor
Bibliographic record
Abstract
Bladder cancer continues to represent a major global health burden, and accurate early staging is crucial for planning an appropriate treatment strategy regarding the presence of detrusor muscle (DM) invasion. This is a prospective observational study, conducted at the Institute of Kidney Diseases, Hayatabad Medical Complex, Peshawar, from January to December 2024, to compare the diagnostic performance of cold-cup biopsy (CCB) over resection biopsy for the early diagnosis of histologically proven DM invasion. Consecutive non-probability sampling was used to enroll 104 patients (52 per group) aged ≥18 years with suspected bladder tumors. Preoperative workup consisted of cystoscopy, imaging, and documentation of the tumor. Methods included CCBs and resectional biopsies. The patients in both groups have comparable demographics and clinical characteristics. The former technique employed targeted mechanical samplings done at the base of the tumor, while the latter used loop excision. The specimens were reviewed by histopathologists blinded to the presence or absence of DM and invasion. The results suggest that although there is no difference in the diagnostic accuracy for muscle invasion of the two methods, the CCB technique provides better DM retrieval (86.5% vs. 65.4%; p = 0.013) and tissue adequacy, which could decrease the rate of re-intervention.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".