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Record W2162463780 · doi:10.1177/0009922809332587

Periurethral Cleaning Prior to Urinary Catheterization in Children: Sterile Water versus 10% Povidone-Iodine

2009· article· en· W2162463780 on OpenAlexaff
Sami Al-Farsi, Maria G. Oliva, Rob Davidson, Susan E. Richardson, Savithiri Ratnapalan

Bibliographic record

VenueClinical Pediatrics · 2009
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineIodineUrinary catheterizationSterile waterUrineSurgeryUnivariate analysisInternal medicineCatheterMultivariate analysis

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare urinary infection rate in children cleaned with sterile water versus a 10% povidone-iodine before bladder catheterization. METHODS: Prospective randomized controlled study of children requiring bladder catheterization in the emergency department whose parents consented to the study were randomly assigned to either of 2 groups, in which sterile water (the "sterile water" group) or 10% povidone-iodine (the "10% povidone-iodine" group) was to be used for peri-urethral cleansing prior to catheterization. RESULTS: The sterile water group had 92 patients and the povidone-iodine group had 94. Most children (87%) were under 12 months of age. Urine cultures were positive in 16% of children in the povidone-iodine group and in 18% in the water group. There was no significant difference in signs and symptoms between the 2 groups. There was no significant association between solution preparation and cultures on univariate regression analysis. CONCLUSIONS: Cleaning the periurethral area of children with sterile water prior to catheterization is not inferior to cleaning with povidone-iodine.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.367
Teacher spread0.323 · 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 designRandomized trial
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

Citations25
Published2009
Admission routes1
Has abstractyes

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