Bladder tumour antigen (BTA stat) test compared to the urine cytology in the diagnosis of bladder cancer: A meta-analysis
Bibliographic record
Abstract
INTRODUCTION: We evaluate the diagnostic value of bladder tumour antigen (BTA stat) tests compared with urine cytology test in detecting bladder cancer. METHODS: We searched public databases including PubMed, MEDLINE Springer, Elsevier Science Direct, Cochrane Library and Google Scholar before December 2012. To collect relevant data of BTA stat tests and urine cytology tests in patients with bladder cancer, we studied meta-analyses of sensitivity, specificity, positive likelihood ratio (LR), negative LR and diagnostic odds ratios (DOR) of BTA stat tests and cytology tests from published studies. We applied the software of Rev. Man 5.1 and Stata 11.0 to the meta-analysis. RESULTS: A total of 13 separate studies consisting of 3462 patients with bladder cancer were considered in the meta-analysis. We found that the BTA stat test had a higher sensitivity than the urine cytology test (0.67, 95% confidence interval [CI] 0.64 to 0.69 vs. 0.43, 95% CI 0.40 to 0.46), but the specificity, positive LR, negative LR, DOR, the area under the curve (AUC) and Q index of the BTA stat test were lower compared with the urine cytology test. The results of the Egger's linear regression test showed no publication bias (p > 0.05). CONCLUSIONS: Specificity, positive LR, negative LR, DOR, the AUC and the Q index of the urine cytology test may be superior to the BTA stat test, but the BTA stat test has greater sensitivity than the urine cytology test.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.060 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".