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Record W4283018108 · doi:10.1016/j.eclinm.2022.101457

Oral pharmacological treatments for chronic prostatitis/chronic pelvic pain syndrome: A systematic review and network meta-analysis of randomised controlled trials

2022· review· en· W4283018108 on OpenAlexaff
Zongshi Qin, Chao Zhang, Jianbo Guo, Joey SW Kwong, Xiao Li, Ran Pang, R. Christopher Doiron, J. Curtis Nickel, Jiani Wu

Bibliographic record

VenueEClinicalMedicine · 2022
Typereview
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsKingston Health Sciences CentreQueen's University
FundersChina Academy of Chinese Medical SciencesNational Natural Science Foundation of ChinaChina Association for Science and Technology
KeywordsMedicineProstatitisDoxazosinChronic prostatitis/chronic pelvic pain syndromeTamsulosinAdverse effectPlaceboQuality of life (healthcare)Internal medicineMeta-analysisOdds ratioPelvic painRandomized controlled trialPhysical therapyAlternative medicineSurgeryProstate

Abstract

fetched live from OpenAlex

Background: Pharmacological treatments for chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) are empirically used. However, the quantitative comparative effectiveness and safety of multiple pharmacological treatments is lacking. Methods: PubMed, Embase, Cochrane Central Register of Controlled Trials, and Web of Science were searched from inception to March 22, 2022. Randomised controlled trials comparing two or more oral pharmacological treatments for patients with CP/CPPS were included. Title, abstract, and full-text screening were independently screened by four reviewers. Primary outcomes were efficacy (the National Institutes of Health Chronic Prostatitis Symptom Index [NIH-CPSI] total score, pain score, urinary score, and quality of life score [QoL]) and safety (adverse events). This study was registered with PROSPERO, CRD42020184106. Findings: = 83%). Interpretation: Pharmacological treatments have little evidence supporting efficacy in CP/CPPS. Future studies could personalise therapy for individuals according to specific symptoms and identify non-pharmacological targets for CP/CPPS. Funding: Dr Jiani Wu received funding for this project from the China Association for Science and Technology (2017QNRC001), the China Academy of Chinese Medical Sciences (ZZ13-YQ-027), and the National Natural Science Foundation of China (82105037).

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)0.064
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.431
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0640.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0760.016
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)0.0150.000

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 head0.388
GPT teacher head0.526
Teacher spread0.138 · 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; both teacher heads agree on what is shown here.

Study designMeta-analysis
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

Citations41
Published2022
Admission routes1
Has abstractyes

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