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Record W2895548764 · doi:10.1097/md.0000000000012459

Hyperthyroidism is not a significant risk of benign prostatic hyperplasia

2018· article· en· W2895548764 on OpenAlexaff
Kee‐Ming Man, Kuen‐Bao Chen, Huey‐Yi Chen, Jen‐Huai Chiang, Yuan‐Chih Su, Samantha S. Man, Dongdong Xie, Yi Wang, Zhiqiang Zhang, Liangkuan Bi, Tao Zhang, Dexin Yu, Wen‐Chi Chen

Bibliographic record

VenueMedicine · 2018
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineHazard ratioConfidence intervalInternal medicineProportional hazards modelPopulationBenign prostatic hyperplasia (BPH)Incidence (geometry)Retrospective cohort studyDiabetes mellitusCohort studyHyperplasiaProstate cancerEndocrinologyCancer

Abstract

fetched live from OpenAlex

Benign prostatic hyperplasia (BPH) is a common disorder in the aging male population. Despite evidence that thyroid status impacts the prostate, the objective of this study was to examine whether patients with hyperthyroidism were at a greater risk for BPH.This study is a retrospective nationwide population-based cohort study of the Chinese population. Data for this study were retrieved from the Taiwan National Health Insurance Research Database (NHIRD). Overall, 1032 male patients aged 40 years or older with hyperthyroidism diagnosed between 2000 and 2006 were included in the hyperthyroidism group, and 4128 matched controls without hyperthyroidism were included in the non-hyperthyroidism group. Both groups were monitored until the end of 2011. A Cox proportional hazards regression model was used to compute and compare the risk of BPH between study participants with and those without hyperthyroidism.Patients with hyperthyroidism exhibited a greater incidence of BPH (18.51% vs 15.53%) than did the controls. Furthermore, the hazard ratio (HR) of the hyperthyroidism group was 1.24 times that of the control group [95% confidence interval (95% CI 1.05-1.46)] signifying that there is a significant 24% increase in the risk of BPH with the presence of hyperthyroidism. This increased risk of BPH with hyperthyroidism, however, failed to remain significant (adjusted HR = 1.11, 95% CI = 0.94-1.3) after adjusting for covariates of age (adjusted HR = 2.72, 95% CI = 2.32-3.2), diabetes (adjusted HR = 1.4, 95% CI = 1.17-1.68), hypertension (adjusted HR = 1.74, 95% CI = 1.49-2.03), hyperlipidemia (adjusted HR = 1.25, 95% CI = 1.03-1.53), neurogenic bladder, cystitis (adjusted HR = 1.23, 95% CI = 0.58-2.59), urethral stricture (adjusted HR = 2.01, 95% CI = 0.28-14.47), urethritis (adjusted HR = 1.52, 95% CI = 0.72-3.21), and urinary tract infection (adjusted HR = 1.77, 95% CI = 1.31-2.39).After adjustment for comorbidities and covariates, hyperthyroidism was not found to be a significant risk factor of BPH in our male study subjects. Further research is warranted to validate our results and elucidate the association of the pathophysiology of these 2 diseases.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.025
GPT teacher head0.291
Teacher spread0.266 · 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".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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