Guideline for the primary care management of male lower urinary tract symptoms
Bibliographic record
Abstract
As my Comment in the first section of the journal suggested, the MTOPS results have offered the possibility to general practitioners of reducing the risk of side‐effects of BPH, particularly urinary retention, by giving patients dual therapy with 5α‐reductase inhibitor and alpha‐adrenergic blocker. Authors from the UK present guidelines for the primary case management of male LUTS, which significantly fills this gap in the literature. Any help in the management of chronic nonbacterial prostatitis is welcome to clinicians; many treatments have been proposed after non‐comparative trials, and so their value must be viewed cautiously. The authors from Canada and USA present the results of a randomized placebo‐controlled study into the use of finasteride in such patients. The other papers in this section all deal with LUTS, e.g. frequency and nocturia, in a variety of situations. There is still great interest in the epidemiology of these symptoms, and in the various methods of grading their severity.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
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".