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Record W2058504323 · doi:10.1136/ebn.11.4.119

Automatic stop orders reduced duration of indwelling urinary catheterisation in hospitalCommentary

2008· letter· en· W2058504323 on OpenAlexaffabout
Jennifer Skelly

Bibliographic record

VenueEvidence-Based Nursing · 2008
Typeletter
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineBlindingUrinary systemCatheterIndwelling catheterUrinary catheterizationSurgeryRandomized controlled trialInternal medicine

Abstract

fetched live from OpenAlex

M Loeb Dr M Loeb, McMaster University, Hamilton, Ontario, Canada; loebm@mcmaster.ca Does a strategy of automatic stop orders reduce duration of indwelling urinary catheterisation in patients in hospital? ### Design: randomised controlled trial. ### Allocation: concealed. ### Blinding: blinded (patients). ### Follow-up period: until catheter removal or discharge from unit. ### Setting: 7 general medical units in 3 hospitals in Hamilton, Ontario, Canada. ### Patients: 692 patients in hospital (mean age 79 y, 62% women) with an indwelling urinary catheter inserted for ⩽48 hours. Patients with symptomatic urinary tract infection or an indwelling catheter inserted in the 10 days before hospital admission were excluded. ### Intervention: automatic stop orders to remove the catheter if the patient did not meet any of 6 criteria for appropriate catheterisation (urinary obstruction, …

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.050
GPT teacher head0.310
Teacher spread0.260 · 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 designNot applicable
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

Citations1
Published2008
Admission routes2
Has abstractyes

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