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The Clinical Efficacy of Percutaneous Nephrolithotomy and Flexible Ureteroscopic Lithotripsy in the Treatment of Calyceal Diverticulum Stones: A Meta-Analysis

2022· review· es· W4289140390 on OpenAlexaboutno aff
Xuefeng Chang, Mingwei Xu, Li Ding, Xiao Wang, Yongqiang Du

Bibliographic record

VenueArchivos Españoles de Urología · 2022
Typereview
Languagees
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPercutaneous nephrolithotomyMedicineCochrane LibraryMeta-analysisLithotripsyMEDLINESurgeryInternal medicinePercutaneous

Abstract

fetched live from OpenAlex

BACKGROUND: The clinical efficacy of percutaneous nephrolithotomy (PCNL) and flexible ureteroscopic lithotripsy (FURL) in the treatment of calyceal diverticulum stones (CDs) remains controversial. We performed a meta-analysis to assess the clinical efficacy of PCNL and FURL in the treatment of CDs. METHODS: We searched a number of relevant electronic databases including China National Knowledge Infrastructure (CNKI), MEDLINE, PubMed, Web of Science, EMBASE, and Cochrane Library until January 31, 2022. STATA 15.1 software was used to analyze all data for this article. The quality of these studies was assessed by the Newcastle-Ottawa Scale (ranged from 0 to 9 stars). RESULTS: Finally, we selected 11 high-quality studies in our meta-analysis,which containing 486 patients. Meta-analysis showed that PCNL had higher stone-free rate [OR=3.55, 95% CI: 2.07 -6.10, P = 0.000], symptom-free rate [OR=3.56, 95% CI: 1.51 -8.38, p= 0.004], while it was slightly inferior to the FURL in bleeding volume [SMD = 1.27, 95% CI: (0.67,1.87), P = 0.000], hospital stay [SMD =2.86, 95% CI: 1.75-3.97, P = 0.000] and complication rate [OR =1.92, 95% CI: 1.10-3.33, P = 0.021], and there was no significant difference in operative time [SMD = -0.011, 95% CI: (-0.41,0.39), P = 0.957]. CONCLUSION: PCNL is safe and effective in the treatment of CDs, which can be considered as the first choice for the clinical treatment of CDs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.310
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.133
GPT teacher head0.406
Teacher spread0.273 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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