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PD26-08 POPULATION BASED EVALUATION OF LABORATORY MONITORING AFTER PRESCRIPTION FOR TESTOSTERONE THERAPY

2020· article· en· W3021416168 on OpenAlexaboutno aff
Jason M. Scovell, Premal Patel, Christopher Wallis, Yonah Krakowsky, Sarah McGriff, Ranjith Ramasamy

Bibliographic record

VenueThe Journal of Urology · 2020
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTestosterone (patch)Medical prescriptionHematocritPopulationInternal medicineProstate-specific antigenGuidelineTestosterone replacementGynecologyUrologyProstateAndrogenHormonePharmacologyPathologyCancer

Abstract

fetched live from OpenAlex

INTRODUCTION AND OBJECTIVE: The testosterone guidelines from both the Endocrine Society and American Urological Association recommend follow-up after starting testosterone therapy with serum testosterone, hemoglobin (or hematocrit), and prostate specific antigen (PSA) within one year. We hypothesized that management patterns differ between prescribing practitioner type. METHODS: We performed a population-based study of men >66 years of age that were newly treated with testosterone replacement therapy between the years 2008 and 2015 in Ontario, Canada using the Ontario Drug Benefit database and the Canadian Institute for Health Information. Patient age, type of therapy, practitioner type, and laboratory value data were extracted. We defined guideline adherence as a measurement of testosterone, hemoglobin (or hematocrit), and prostate specific antigen (PSA) within one year of testosterone prescription. Comparisons between these values across prescription year were evaluated. Proportional data was evaluated using a Chi-Square test on PRISM 8. RESULTS: A total of 15,503 men >65y received testosterone therapy between 2008 and 2015. Testosterone therapy prescriptions peaked in 2013 at n=2,434 and declined to n=1,831 in 2015. The majority of patients were between 66-70 years of age (61%, n=9,387), followed by men aged 75+ (23%, n=3,568), and men aged 71-74 (16%, n=2,548). Testosterone therapy included topical gels (48%, n=7,490), oral (27%, n=4,187), injections (23%, n=3,551), and patches (2%, n=275). Practitioners including general practitioners/family practice (67%, n=10,358), urologists (15%, n=2,387), and endocrinologists (6%, n=865) were the top three prescribing specialties. Guideline adherence amongst all practitioners improved from 2008 at a rate of 16% to a peak of 63% in 2014 but declined to 49% in 2015. Peak guideline adherence was different amongst each top prescribing specialties (p<0.05) and was led by Urologists in 2014 at 80%, followed by Endocrinologists at 64%, and GP/FP at 59%. This discrepancy was likely due to lower PSA test rates amongst GP/FP (73%) and endocrinologists (66%) compared to Urologists (86%; all in 2014). CONCLUSIONS: Appropriate laboratory management, defined as a laboratory test for testosterone, hemoglobin (or hematocrit), and PSA within one year of testosterone prescription was best achieved by Urologists and less than half the general practitioners. The decline in frequency of PSA testing among general practitioners prescribing testosterone therapy could reflect the practice pattern of decrease in prostate cancer screening. Source of Funding: None.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.651

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.068
GPT teacher head0.341
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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