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Record W4312473247 · doi:10.26443/mjm.v20i2.330

An Approach to the Management of Lower Urinary Tract Symptoms Secondary to Benign Prostatic Hyperplasia.

2022· article· en· W4312473247 on OpenAlexaffvenueabout
David Bouhadana, Iman Sadri

Bibliographic record

VenueMcGill Journal of Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineLower urinary tract symptomsHyperplasiaUrologyBenign prostatic hyperplasia (BPH)Quality of life (healthcare)Urinary systemMedical diagnosisProstateGynecologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Benign prostatic hyperplasia (BPH) is a condition that affects up to 50% of men over the age of 50; the condition’s prevalence increases with age, particularly after the age of 40. BPH can lead to lower urinary tract symptoms (LUTS) which can have a significant negative impact on health-related quality of life (HRQoL). Men presenting with a gradual onset of LUTS are often suspected to have BPH. However, the clinician must recognize that LUTS possess many different aetiologies. This article aims to provide medical students with a stepwise approach to the diagnosis and management of LUTS that are secondary to BPH. The outlined approach describes the differential diagnoses, required investigations, and management-related details for LUTS that are secondary to BPH. This approach is based off of relevant Canadian, American, and European urological association guidelines.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.003

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 head0.032
GPT teacher head0.320
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2022
Admission routes3
Has abstractyes

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