Fusion Biopsy, not Cognitive, Is the New Gold Standard
Bibliographic record
Abstract
To date, although some benefits resulting from a software-guided technique are undeniable, no clear superiority of fusion over cognitive targeted biopsy (COG-TB) has been supported by strong evidence. We discuss potential causes of trials failing to show the superiority of fusion TB (FUS-TB) and highlight its advantages over the cognitive approach.One possible reason why current literature showed contradictory evidence in supporting FUS-TB may be the lack of high-quality well-designed trials. Indeed, most of the studies addressing this issue have considerable limitations, such as underpowering, small sample size, lack of randomization, and poor generalizability. A second reason may be the inclusion in the majority of trials of a wide spectrum of MRI-lesions, a scenario in which the benefits of FUS-TB may be less evident. In fact, some of the few studies considering smaller targets demonstrated higher accuracy for the FUS technique. As concerns the advantages of FUS-TB, the opportunity offered by some fusion systems of storing information useful for planning and/or follow-up active surveillance, focal therapy, and radical prostatectomy, as well as a reported faster learning curve, are strong points supporting the fusion approach.In conclusion, the potential advantages when targeting smaller lesions together with the storage capability to guide patient management after the biopsy and an easier learning curve may make the FUS approach the more appropriate technique for performing TB.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".