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Record W4362665551 · doi:10.21203/rs.3.rs-2550212/v1

High Prevalence of Erectile Dysfunction in Men With Hyperthyroidism: a meta-analysis

2023· preprint· en· W4362665551 on OpenAlexaboutno aff
Xiaowen Liu, Yanling Wang, Li Ma, Danhui Wang, Zhihong Peng, Zenghui Mao

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineErectile dysfunctionMeta-analysisIncidence (geometry)Internal medicinePediatrics

Abstract

fetched live from OpenAlex

Abstract Objective: The purpose of the study was to evaluate the association between hyperthyroidism and risk of erectile dysfunction (ED). Methods: Pubmed, Embase, Cochrane, and Web of Science databases were searched for all studies evaluating men with hyperthyroidism who had erectile dysfunction, and the Newcastle-Ottawa Quality Rating Scale to evaluate the quality of studies for meta-analysis, and Stata 16.0, RevMan 5.3 software was used for Meta-analysis. Results: A total of 4 papers with 25519 study subjects were included, of which the number of patients suffering from hyperthyroidism was 6429 and the number of controls was 19090. The overall prevalence of ED in patients with comorbid hyperthyroidism was 31.1% (95% CI 0.06-0.56). The incidence of ED in patients with combined uncomplicated hyperthyroidism was 21.9% (95% CI 0.05-0.38). The ED increased significantly in the group with hyperthyroidism in four studies (OR: 1.73; 95% CI [1.46-2.04]; p < .00001). Conclusion: Our findings demonstrates that patients with hyperthyroidism had more incidence of ED. These data can inform discussion between physicians and patients with hyperthyroidism regarding the choice of therapy for ED.

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)0.007
metaresearch head score (Gemma)0.013
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: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.034
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.248
GPT teacher head0.425
Teacher spread0.177 · 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
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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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