The Changing Role of the Primary Care Physician in the Management of Urologic Conditions.
Bibliographic record
Abstract
am very pleased to introduce our latest Supplement in the series: Urology Update for Primary Care Physicians. In 2008 we came to the conclusion that the primary care physician (PCP) had a dramatic role and impact in diagnosing and managing a number of urologic conditions that affect their patients. To that end we have published a number of updates always looking at needs assessments from the PCP. If anything, the present Supplement only emphasizes this role. The PCP, with the knowledge and understanding gained from these reviews should feel comfortable in diagnosing a number of these conditions and offering strategies of treatment that we have suggested. If the patient does not respond as expected, or if there are any concerns, then the PCP can very easily refer to the specialist. To provide backup for the PCP, in certain conditions we have highlighted “RED FLAGS” denoting symptoms or signs that require early referral. In this Supplement we have attempted to emphasize the importance in trying to encourage the patient to report the symptoms suggestive of these pervasive conditions (Overactive Bladder – Radomski, 1 BPH – Elterman, 2 and Nocturia – Barkin 3 ), because there are excellent, effective and safe ways of treating the conditions that can have a dramatic effect on the patients’ lives. We also highlight the fact that there have been reports of potential risks and side effects of some of the frequently prescribed old and new medical therapies. We try to provide the PCP the information needed to dispel some of the myths associated with, for example, Testosterone Replacement Therapy and increased cardiac risk, (Hassan 4 ). Finally, in our review of the old and new medical therapies for the management of, first castrate sensitive and then castrate resistant prostate cancer, we highlight the critical role that the PCP plays in helping to identify early and manage some of the expected and unexpected side effects of these life saving therapies, (Mak 5 ).
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".