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Record W4362597176 · doi:10.1159/000530520

Why Should Orchidopexy Be Performed in Congenital Hypogonadotropic Hypogonadism, and When?

2023· article· en· W4362597176 on OpenAlexaff
Meriem Bensalah-Hammoutene, Guy Van Vliet

Bibliographic record

VenueHormone Research in Paediatrics · 2023
Typearticle
Languageen
FieldMedicine
TopicTesticular diseases and treatments
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsHypogonadotropic hypogonadismTesticular cancerHypogonadotrophic hypogonadismMedicineKallmann syndromeDelayed pubertyPopulationGynecologyFertilityContext (archaeology)EndocrinologyInternal medicineCancerHormoneBiologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: In otherwise normal boys with undescended testes, early orchidopexy is recommended to preserve fertility, to decrease the risk of testicular cancer, and to facilitate its detection. Indeed, compared to the general population, the risk of testicular cancer is increased two- to eight-fold in isolated cryptorchidism and usually occurs before the age of 40 years. By contrast, when cryptorchidism is associated with congenital hypogonadotropic hypogonadism, the risk of testicular cancer is unknown. OBJECTIVE: The aim of this study was to determine the characteristics of testicular cancer when cryptorchidism is associated with congenital hypogonadotropic hypogonadism. METHODS: We conducted a PubMed research without date limits including the following key words: hypogonadism, hypogonadotropic hypogonadism, testicular cancer, testicular germ cell tumors, undescended testis, Kallmann syndrome, FSH, AFP (α foeto protein), βHCG. RESULTS: Only 3 patients with testicular cancer and congenital hypogonadotropic hypogonadism have been published in the past 4 decades and two were diagnosed at 50 and 64 years. CONCLUSION: Gonadotropin deficiency may protect against testicular cancer, and orchidopexy in this context may be deferred.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.064
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.140
GPT teacher head0.380
Teacher spread0.240 · 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 teacher head, 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

Citations1
Published2023
Admission routes1
Has abstractyes

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