353 TESTOSTERONE BREAKTHROUGH ON LUTEINIZING HORMONE-RELEASING HORMONE AGONISTS: THE SIZE OF THE PROBLEM AND PREDISPOSING FACTORS
Bibliographic record
Abstract
You have accessJournal of UrologyProstate Cancer: Localized1 Apr 2011353 TESTOSTERONE BREAKTHROUGH ON LUTEINIZING HORMONE-RELEASING HORMONE AGONISTS: THE SIZE OF THE PROBLEM AND PREDISPOSING FACTORS Tom Pickles and Scott Tyldesley Tom PicklesTom Pickles Vancouver, Canada More articles by this author and Scott TyldesleyScott Tyldesley Vancouver, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2011.02.438AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES To describe, in a large population database, the comprehensiveness of testosterone (TT) suppression while on Luteinizing Hormone-Releasing Hormone (LHRH) agonists. To describe the incidence of breakthrough above conventional castrate levels: 50ng/dl, 32ng/dl, 20ng/dl. To identify predisposing factors. METHODS From 11,752 consecutive patients treated with curative radiation therapy 1998–2007, 2290 met the following criteria: continuous LHRH therapy, TT monitoring, at least 3 months of ADT, no more than 12 months neoadjuvant ADT. Prescribing records, TT measures and patient demographic information were extracted from Provincial databases. Frequency of breakthrough above conventional castrate levels (as defined above, a priori) were calculated and related to patient and pharmacologic factors. RESULTS The median duration of LHRH was 11.3 months and a mean of 5.5 injections were administered. The risk of breakthrough per LHRH injection is 18.9% ≥20ng/dl, 4.2% ≥ 32ng/dl, and 2.2% ≥ 50ng/dl. Per patient course of ADT, the breakthrough risk was higher: 26.8% ≥20ng/dl, 6.6%≥ 32ng/dl and 3.3% ≥50ng/dl. Predisposing factors are younger age, p<0.001 and higher body mass index, p=0.008, but not pre-treatment testosterone, p=0.4. Age and BMI are correlated (younger men being thinner, p<0.001) and BMI with pre-treatment TT (obese men having lower TT levels, p<0.001). Logistic regression was carried out: Young age and high BMI were highly associated with the risk of a breakthrough (p<0.001 and p=0.01 respectively, see figure) and high pre-treatment TT weakly so (p=0.052). Differences in breakthrough rate according to the LHRH preparation used were observed, but were not statistically different. CONCLUSIONS Breakthroughs of TT after LHRH are common, 3–27% depending on definition. They are commoner in the young and obese. Monitoring of TT is required. © 2011 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 185Issue 4SApril 2011Page: e143-e144 Advertisement Copyright & Permissions© 2011 by American Urological Association Education and Research, Inc.MetricsAuthor Information Tom Pickles Vancouver, Canada More articles by this author Scott Tyldesley Vancouver, Canada More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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".