2291 TESTOSTERONE PRESCRIBING PATTERNS IN THE MALE INFERTILITY POPULATION
Bibliographic record
Abstract
You have accessJournal of UrologyInfertility: Evaluation1 Apr 20132291 TESTOSTERONE PRESCRIBING PATTERNS IN THE MALE INFERTILITY POPULATION Mary Samplaski, Yasir Loai, Kirk Lo, Ethan Grober, and Keith Jarvi Mary SamplaskiMary Samplaski Toronto, Canada More articles by this author , Yasir LoaiYasir Loai Toronto, Canada More articles by this author , Kirk LoKirk Lo Toronto, Canada More articles by this author , Ethan GroberEthan Grober Toronto, Canada More articles by this author , and Keith JarviKeith Jarvi Toronto, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2013.02.2232AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Over the last decade there has been a gradual increase in testosterone (T) prescribing. We sought to analyze patterns of T prescribing in men presenting for infertility evaluation. METHODS Men presenting for fertility evaluation from 2008-2012 on T were identified via a prospective database. Data were analyzed for prevalence, prescriber, formulation, dosage and indication. RESULTS 4400 men were evaluated for male infertility, and 56 (1.3%) were on T at presentation. Prescribers included: Endocrinologists (10, 17.9%), General Practitioners (5, 8.9%), Urologists (3, 5.3%), and independently obtained (4, 7.1%). Formulations and dosages included: Gel (26, 46.4%): most commonly 5mg every other day (QOD), range: 5mg QOD to 10mg daily; Intramuscular injection (25, 44.6%), most commonly 200mg every 2 weeks, range: 50-300mg every 2 weeks; Oral (1, 1.8%), 80mg QOD; Pellet (1, 1.7%), dose unknown; and unknown formulation and dose (5, 8.9%). Indications for T included: symptoms of hypogonadism (27, 48.2%), symptoms + low serum T (21, 37.5%), low serum T (4, 7.1%), athletic purposes (3, 5.4%), and subfertility (1, 1.8%). Co-existing conditions included: Klinefelters syndrome (8, 14.3%), history of bilateral undescended testicles (7, 12.5%), Kallmans syndrome (5, 8.9%), Sertoli only syndrome (2, 3.6%), chemotherapy induced testicular failure (2, 3.6%), prolactinoma (2, 3.6%), anejaculation (1, 1.8%), and opioid induced testicular failure (1, 1.8%). CONCLUSIONS At our infertility center, T was not commonly used by men presenting for infertility investigation. Most men on T were being treated for appropriate conditions, with appropriate routes and dosages. Endocrinologists and General Practitioners were the most common prescribers, and educational efforts to emphasize the negative impacts of T on spermatogenesis should be focused on these groups. There are a group of men that obtain their T independently, and a group that uses T for athletic purposes. While this was a small fraction of the men in our population of infertile men, as the use of T increases, this fraction will undoubtedly grow. © 2013 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 189Issue 4SApril 2013Page: e939 Peer Review Report Advertisement Copyright & Permissions© 2013 by American Urological Association Education and Research, Inc.MetricsAuthor Information Mary Samplaski Toronto, Canada More articles by this author Yasir Loai Toronto, Canada More articles by this author Kirk Lo Toronto, Canada More articles by this author Ethan Grober Toronto, Canada More articles by this author Keith Jarvi Toronto, Canada More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".