Surveillance of non-muscle invasive bladder cancer using fluorescence in situ hybridization
Bibliographic record
Abstract
BACKGROUND: To evaluate the diagnostic effectiveness and predictive value of fluorescence in situ hybridization (FISH) in the surveillance of non-muscle invasive bladder cancer (NMIBC). METHODS: PubMed/Medline, EMBASE, Web of Science, Ovid, Web of Knowledge, and Cochrane Library will be searched for studies related to the topic. The identification, inclusion, and exclusion flowcharts will be conducted according to preferred reporting items for systematic reviews and meta-analysis guidelines. The identified reports will be critically appraised according to the Newcastle-Ottawa scale, quality assessment of diagnostic accuracy studies-2 and standards for reporting of diagnostic accuracy 2015. Forest plots will be generated to display hazard ratios, sensitivities, and specificities. Pooled estimates with their 95% confidence intervals will be calculated using the bivariate model, the hierarchical summary receiver operating characteristic model and a fixed- or random-effects model. RESULTS: This study will provide evidence and data to form a comprehensive understanding of the value of FISH in the surveillance of NMIBC. CONCLUSION: The diagnostic efficacy of FISH will be affected by post-therapy factors. However, FISH still could facilitate the surveillance of NMIBC owing to its non-invasive feature. This study will improve the clinical decision-making and enlighten the future research of NMIBC.
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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 | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".