Focal Laser Ablation for Localized Prostate Cancer
Bibliographic record
Abstract
Throughout history, medicine has witnessed paradigm shifts that significantly change patient treatment. In surgical oncology, the introduction of lumpectomy revolutionized breast cancer treatment while partial nephrectomy has altered the management of kidney cancer. In both cases, organ preservation is combined with efficacious management of the cancer via a less invasive approach. Within urology, prostate cancer (PCa) may be the next to benefit from such a treatment paradigm. Current management of PCa involves either whole organ treatment, with the inherent side effects, while selected patients are eligible for active surveillance. Focal therapy offers a middle ground for low-risk patients with PCa, again using the principles of a minimally invasive treatment of the cancer, in this case using an energy source with few side effects, combined with maximal organ preservation. Because focal therapy for PCa is still in evolution, there is no consensus on the ideal energy source that should be used to ablate the PCa, imaging to monitor the tissue destruction in real time, how many treatments may be offered, and the ideal follow-up regimen. Long-term follow-up of patients is needed before it is recommended as a first-line treatment. Nevertheless, evidence is accumulating that radically treating PCa holds survival benefit for patients; however, the number of men needed to treat is considerable, with significant side effects; thus, more centers are investigating focal therapy as an option. This review focuses on the use of the laser as the energy source for focal ablation, while bringing historically relevant information regarding laser energy and highlighting the perceived advantageous of focal laser ablation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".