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Record W4283071954 · doi:10.1016/s2666-7568(22)00096-4

Adverse cardiovascular events and mortality in men during testosterone treatment: an individual patient and aggregate data meta-analysis

2022· review· en· W4283071954 on OpenAlexaff
Jemma Hudson, Moira Cruickshank, Richard Quinton, Lorna Aucott, Magaly Aceves‐Martins, Katie Gillies, Shalender Bhasin, Peter J. Snyder, Susan S. Ellenberg, Mathis Grossmann, Thomas G. Travison, Emily Gianatti, Yvonne T. van der Schouw, Mariëlle H. Emmelot‐Vonk, Erik J. Giltay, Geoff Hackett, Sudarshan Ramachandran, Johan Svartberg, Kerry L. Hildreth, Kristina Groti Antonič, Gerald Brock, J. Lisa Tenover, Hui Meng Tan, Christopher C.K. Ho, Wei Shen Tan, Leonard S. Marks, Richard Ross, Robert S. Schwartz, Paul Manson, Stephen A. Roberts, Marianne Andersen, Line Velling Magnussen, Rodolfo Hernández, Nick Oliver, Frederick C. W. Wu, Waljit S. Dhillo, Siladitya Bhattacharya, Miriam Brazzelli, Channa Jayasena

Bibliographic record

VenueThe Lancet Healthy Longevity · 2022
Typereview
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsWestern University
FundersNational Institute on AgingHealth Technology Assessment ProgrammeBiotechnology and Biological Sciences Research CouncilManchester Biomedical Research CentreChief Scientist Office, Scottish Government Health and Social Care DirectorateNational Institutes of HealthTeva Pharmaceutical IndustriesNational Institute for Health and Care ResearchNorth Staffordshire Medical InstituteMedical Research CouncilAbbVieUniversity of Aberdeen
KeywordsMeta-analysisTestosterone (patch)Adverse effectMedicineInternal medicineAggregate dataAggregate (composite)Intensive care medicinePathology

Abstract

fetched live from OpenAlex

Background: Testosterone is the standard treatment for male hypogonadism, but there is uncertainty about its cardiovascular safety due to inconsistent findings. We aimed to provide the most extensive individual participant dataset (IPD) of testosterone trials available, to analyse subtypes of all cardiovascular events observed during treatment, and to investigate the effect of incorporating data from trials that did not provide IPD. Methods: We did a systematic review and meta-analysis of randomised controlled trials including IPD. We searched MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, MEDLINE Epub Ahead of Print, Embase, Science Citation Index, the Cochrane Controlled Trials Register, Cochrane Database of Systematic Reviews, and Database of Abstracts of Review of Effects for literature from 1992 onwards (date of search, Aug 27, 2018). The following inclusion criteria were applied: (1) men aged 18 years and older with a screening testosterone concentration of 12 nmol/L (350 ng/dL) or less; (2) the intervention of interest was treatment with any testosterone formulation, dose frequency, and route of administration, for a minimum duration of 3 months; (3) a comparator of placebo treatment; and (4) studies assessing the pre-specified primary or secondary outcomes of interest. Details of study design, interventions, participants, and outcome measures were extracted from published articles and anonymised IPD was requested from investigators of all identified trials. Primary outcomes were mortality, cardiovascular, and cerebrovascular events at any time during follow-up. The risk of bias was assessed using the Cochrane Risk of Bias tool. We did a one-stage meta-analysis using IPD, and a two-stage meta-analysis integrating IPD with data from studies not providing IPD. The study is registered with PROSPERO, CRD42018111005. Findings: 16 of 176). Overall, patient age (interaction 0·97 [99% CI 0·92-1·03]; p=0·17), baseline testosterone (interaction 0·97 [0·82-1·15]; p=0·69), smoking status (interaction 1·68 [0·41-6·88]; p=0.35), or diabetes status (interaction 2·08 [0·89-4·82; p=0·025) were not associated with cardiovascular risk. Interpretation: We found no evidence that testosterone increased short-term to medium-term cardiovascular risks in men with hypogonadism, but there is a paucity of data evaluating its long-term safety. Long-term data are needed to fully evaluate the safety of testosterone. Funding: National Institute for Health Research Health Technology Assessment Programme.

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)0.034
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.034
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.053
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0180.068
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.384
GPT teacher head0.415
Teacher spread0.031 · 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 designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations102
Published2022
Admission routes1
Has abstractyes

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