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Record W1998459730 · doi:10.5489/cuaj.337

Best practices for the treatment and prevention of urinary tract infection in the spinal cord injured population

2013· article· en· W1998459730 on OpenAlexafffundvenueabout
Timothy C. Hill, Richard Baverstock, Kevin V. Carlson, Eric Estey, Gary J. Gray, Denise C. Hill, Chester Ho, Rosemary McGinnis, Katherine Moore, Raj Parmar

Bibliographic record

VenueCanadian Urological Association Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsFoothills Medical CentreSpinal Cord Injury AlbertaUniversity of AlbertaUniversity of CalgaryAlberta Health ServicesSpinal Cord Injury BC
FundersUniversity of AlbertaGovernment of Alberta
KeywordsMedicineContext (archaeology)Spinal cord injuryBest practiceProfessional associationPopulationNursingSpinal cordPsychiatryPolitical science

Abstract

fetched live from OpenAlex

The purpose of this review of clinical guidelines and best practices literature is to suggest prevention options and a treatment approach for intermittent catheter users that will minimize UTI. Recommendations are based both on evidence in the literature and an understanding of what is currently attainable within the Alberta context, through collaboration between both major tertiary care centres (Edmonton and Calgary) and between various professionals who regularly encounter these patients, including nursing, physiatry, and urology. Key references used to prepare this document included Canadian sources such as; SCIRE, along with the American Urological Association (AUA) and European Urological Association (EUA) documents on the topic and resources from Paralyzed Veterans of America. No details on neurogenic bladder management exist in the Canadian Urological Association list of publications. Finally, the impetus for preparing these protocols arose due to concerns identified by a survey of individuals with SCI regarding perceived gaps in knowledge and practice among caregivers and physicians about SCI and UTI prevention and management.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.079
GPT teacher head0.377
Teacher spread0.297 · 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 designObservational
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

Citations41
Published2013
Admission routes4
Has abstractyes

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