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Examining Differences in Pathological Outcomes and Safety for Prostate Cancer Patients Undergoing Either Subcapsular Orchiectomy or Medical Androgen Deprivation Therapy: A Systematic Review

2025· review· en· W4408122209 on OpenAlexaboutno aff
Erick Frapancah, Indrawarman Soerohardjo

Bibliographic record

VenueF1000Research · 2025
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsAndrogen deprivation therapyMedicineProstate cancerOrchiectomyPathologicalOpen peer reviewPhysiologyOncologyAndrogenInternal medicineUrologyCancerBioinformaticsPlant biologyHormoneBiology

Abstract

fetched live from OpenAlex

Purpose: The purpose of this systematic review is to investigate differences in pathological outcomes and safety between subcapsular orchiectomy and pharmacological androgen deprivation therapy (ADT) for prostate cancer management. Methods: A systematic search was conducted on PubMed, Google Scholar, and ScienceDirect for original articles published until February 2024 that compared tumour characteristics, biochemical markers, and adverse effects associated with these treatments. The risk of bias from each study was assessed using the Newcastle Ottawa Scale and Risk of Bias-2 (ROB-2) tool. Results: Eleven studies meeting the inclusion criteria were analysed. Both subcapsular orchiectomy and pharmacological ADT effectively reduced tumour size and prostate-specific antigen (PSA) levels. Subcapsular orchiectomy was linked to higher surgical complication rates. At the same time, due to its systemic pharmacological mechanisms, pharmacological ADT carries a greater risk of metabolic side effects, such as weight gain and insulin resistance. Conclusions: Both subcapsular orchiectomy and pharmacological ADT are viable options for prostate cancer treatment. However, their differing safety and pharmacological profiles highlight the need for personalised treatment strategies based on individual patient factors and preferences. PROSPERO registration: CRD42025634678 (17/01/2025).

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.309
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.134
GPT teacher head0.422
Teacher spread0.288 · 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 designSystematic review
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

Citations0
Published2025
Admission routes1
Has abstractyes

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