A narrative review of pelvic lymph node dissection in prostate cancer
Bibliographic record
Abstract
Pelvic lymph node dissection (PLND) is an important component in the staging and prognostication of prostate cancer. We performed a narrative review to assess the literature surrounding PLND: (I) the current guideline recommendations and contemporary utilization, (II) the calculation of patient-specific risk to perform PLND using available nomograms, (III) to review the extent of dissection, and its associated outcomes and complications. Due to the improved lymph node yield, better staging, and theoretical improvement in the control of micro-metastatic disease, guidelines have supported the use of (extended-) PLND in patients deemed to be at intermediate or high risk of lymph node involvement (often at a threshold of 5% on modern risk nomograms). However, in practice, real-world utilization of PLND varies considerably due to multiple reasons. Conflicting evidence persists with no clear oncological benefit to PLND, and a small, but important, risk of morbidity. Complications are rare, but include lymphoceles; thromboembolic events; and more rarely, obturator nerve, vascular, and ureteric injury. Furthermore, changing disease incidence and stage migration in the context of earlier detection overall have led to a decreased risk of nodal disease. The trade-offs between the benefits, harms, and risk tolerance/threshold must be carefully considered between each patient and their clinician.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".