Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this article is to review the recent literature concerning obstructive uropathy from advanced cancer. This factor is relevant as it is often a difficult task for physicians to estimate a patient's life expectancy and evaluate the possible benefit of urinary diversion. Recent research has addressed this issue. We now have objective criteria to stratify the possible benefit of urinary diversion in patients with malignant obstructive nephropathy. RECENT FINDINGS: When dealing with ureteric obstruction treatment must be individualized and risk stratification is of paramount importance. There is no clear evidence that urinary diversion improves quality of life and treatment decisions must be individualized. The decision to treat obstructive nephropathy in the palliative setting should include the patient, his family and members of the support team. SUMMARY: Ureteric stents and percutaneous nephrostomies are the preferred initial treatment options, although stent infection, encrustation and blockages are common problems. New compression-resistant metallic stents seem promising for patients with a malignant disease who require long-term urinary drainage. When more conservative measures have failed, supra-vesical reconstruction and diversion may be an option.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.014 | 0.004 |
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".