Bibliographic record
Abstract
PURPOSE OF REVIEW: To review recent literature related to urologic malignancies in patients with neurogenic lower urinary tract dysfunction (NLUTD). We performed a literature search of electronic databases (PubMed, ScienceDirect, Scopus, and CIANHL), with a focus on articles published between January 2015 and December 2019. RECENT FINDINGS: Recent reports demonstrate a lower incidence of bladder cancer in the NLUTD population than previously found, although still significantly higher than the general population. Bladder cancer in patients with NLUTD is usually diagnosed at a younger age, and is associated with higher rates of squamous cell cancer, a higher stage at presentation, and increased mortality. Evidence for screening for bladder cancer in NLUTD is conflicting, with no formal protocols proven for general use. NLUTD has been shown to have a lower rate of prostate cancer, and may be associated with an earlier diagnosis of renal cancer. SUMMARY: Genitourinary malignancies, although still rare, are an important source of morbidity and mortality in patients with NLUTD. Physicians should recognize that bladder cancer in NLUTD is often a lethal disease. Further research is needed to assist physicians with early recognition of these malignancies to improve patient outcomes.
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) | 0.005 | 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".