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Guidelines for the Prevention, Diagnosis, and Management of Urinary Tract Infections in Pediatrics and Adults

2024· review· en· W4404024254 on OpenAlexaff
Zachary Nelson, Abdullah Tarık Aslan, Nathan P. Beahm, Michelle Blyth, Matthew Cappiello, Danielle Casaus, Fernándo Domínguez, Susan Egbert, Alexandra Hanretty, Tina Khadem, Katie B. Olney, Ahmed Abdul-Azim, Gloria Aggrey, Daniel T. Anderson, Mariana Barosa, Michaël Bosco, Elias B. Chahine, Souradeep Chowdhury, Alyssa Christensen, Daniela de Lima Corvino, Margaret A. Fitzpatrick, Molly Fleece, Brent Footer, Emily M. Fox, Bassam Ghanem, Fergus Hamilton, Justin F Hayes, Boris Jegorovic, Philipp Jent, Rodolfo Norberto Jiménez-Juárez, Annie Joseph, Minji Kang, Geena Kludjian, Sarah Kurz, Rachael A Lee, Todd C. Lee, Timothy Li, Alberto Enrico Maraolo, Mira Maximos, Emily G. McDonald, Dhara Mehta, William J Moore, Cynthia T. Nguyen, Cihan Papan, Akshatha Ravindra, Brad Spellberg, Robert E. Taylor, Alexis Thumann, Steven Y. C. Tong, Michael P. Veve, James R. Wilson, Arsheena Yassin, Veronica B Zafonte, Alfredo J Mena Lora

Bibliographic record

VenueJAMA Network Open · 2024
Typereview
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsNewfoundland and Labrador Centre for Applied Health ResearchMemorial University of NewfoundlandWomen's College HospitalUniversity of TorontoMcGill UniversityUniversity of ManitobaUniversity of Alberta
Fundersnot available
KeywordsGuidelineMedicineSystematic reviewMEDLINEEvidence-based practiceFamily medicineIntensive care medicineAlternative medicinePathologyPolitical science

Abstract

fetched live from OpenAlex

Importance: Traditional approaches to practice guidelines frequently result in dissociation between strength of recommendation and quality of evidence. Objective: To create a clinical guideline for the diagnosis and management of urinary tract infections that addresses the gap between the evidence and recommendation strength. Evidence Review: This consensus statement and systematic review applied an approach previously established by the WikiGuidelines Group to construct collaborative clinical guidelines. In May 2023, new and existing members were solicited for questions on urinary tract infection prevention, diagnosis, and management. For each topic, literature searches were conducted up until early 2024 in any language. Evidence was reported according to the WikiGuidelines charter: clear recommendations were established only when reproducible, prospective, controlled studies provided hypothesis-confirming evidence. In the absence of such data, clinical reviews were developed discussing the available literature and associated risks and benefits of various approaches. Findings: A total of 54 members representing 12 countries reviewed 914 articles and submitted information relevant to 5 sections: prophylaxis and prevention (7 questions), diagnosis and diagnostic stewardship (7 questions), empirical treatment (3 questions), definitive treatment and antimicrobial stewardship (10 questions), and special populations and genitourinary syndromes (10 questions). Of 37 unique questions, a clear recommendation could be provided for 6 questions. In 3 of the remaining questions, a clear recommendation could only be provided for certain aspects of the question. Clinical reviews were generated for the remaining questions and aspects of questions not meeting criteria for a clear recommendation. Conclusions and Relevance: In this consensus statement that applied the WikiGuidelines method for clinical guideline development, the majority of topics relating to prevention, diagnosis, and treatment of urinary tract infections lack high-quality prospective data and clear recommendations could not be made. Randomized clinical trials are underway to address some of these gaps; however further research is of utmost importance to inform true evidence-based, rather than eminence-based practice.

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)0.065
metaresearch head score (Gemma)0.304
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.345

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.304
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0130.010
Science and technology studies0.0020.003
Scholarly communication0.0070.007
Open science0.0070.006
Research integrity0.0090.012
Insufficient payload (model declined to judge)0.0090.008

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 head0.114
GPT teacher head0.428
Teacher spread0.314 · 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
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

Citations77
Published2024
Admission routes1
Has abstractyes

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