MRI-Guided Laser Ablation for Localized Prostate Cancer
Bibliographic record
Abstract
Throughout medical history, new innovations and novel thinking have generated paradigm shifts that significantly changed patient treatment. Organ-preserving therapies are widely accepted in many facets of medicine and more recently in oncology. For example, partial nephrectomy is now accepted as a preferred alternative over radical nephrectomy for small (up to 4 cm or T1) tumors. Focal therapy is an organ-preserving strategy applying energy (cryotherapy, laser ablation, high-intensity focused ultrasound) to destroy tumors but leaving the majority of the organ and its surrounds unscathed and functional. As more concerns are raised with the common whole-gland treatment due to the perceived indolent nature of prostate cancer (PCa) and the morbidity that accompanies all the current offered treatment options, more patients and physicians are searching for a treatment alternative. Perhaps PCa may be the next disease to benefit from a treatment paradigm shift. This chapter focuses on the use of laser as the energy source for focal ablation of PCa, especially under magnetic resonance imaging (MRI) guidance and highlights the perceived advantageous of focal laser ablation. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".