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Record W4387328467 · doi:10.1016/s2666-7568(23)00169-1

Symptomatic benefits of testosterone treatment in patient subgroups: a systematic review, individual participant data meta-analysis, and aggregate data meta-analysis

2023· review· en· W4387328467 on OpenAlexaff
Jemma Hudson, Moira Cruickshank, Richard Quinton, Lorna Aucott, Frederick C. W. Wu, Mathis Grossmann, Shalender Bhasin, Peter J. Snyder, Susan S. Ellenberg, Thomas G. Travison, Gerald Brock, 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, 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, Magaly Aceves‐Martins, Katie Gillies, Rodolfo Hernández, Nick Oliver, Waljit S. Dhillo, Siladitya Bhattacharya, Miriam Brazzelli, Channa Jayasena

Bibliographic record

VenueThe Lancet Healthy Longevity · 2023
Typereview
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsWestern University
FundersFP7 HealthHealth Technology Assessment ProgrammeManchester Biomedical Research CentreChief Scientist Office, Scottish Government Health and Social Care DirectorateMedical Research CouncilDirectorate for Biological SciencesNational Institutes of HealthScottish GovernmentImperial College LondonNIHR Imperial Biomedical Research CentreBiotechnology and Biological Sciences Research CouncilDepartment of Health and Social CareNational Institute for Health and Care ResearchUniversity of Aberdeen
KeywordsMeta-analysisAggregate dataAggregate (composite)Systematic reviewPsychologyPatient dataClinical psychologyMedicineEconometricsMEDLINEStatisticsComputer scienceInternal medicineMathematicsBiology

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.028
metaresearch head score (Gemma)0.058
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 score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.058
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0250.050
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.002
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 head0.662
GPT teacher head0.468
Teacher spread0.194 · 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

Citations32
Published2023
Admission routes1
Has abstractyes

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