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Record W1969364365 · doi:10.5489/cuaj.1320

The effect of a 6 Fr catheter in women: Are they obstructive?

2013· article· en· W1969364365 on OpenAlexaffvenue
Patrick O. Richard, Nydia Icaza Ordonez, Le Mai Tu

Bibliographic record

VenueCanadian Urological Association Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineCatheterBladder outlet obstructionLumen (anatomy)Inclusion and exclusion criteriaUrologyOverdiagnosisSurgeryInternal medicineProstate

Abstract

fetched live from OpenAlex

OBJECTIVES: Our objective was to evaluate the effect of a 6 Fr transurethral catheter on the uroflowmetry and to assess whether it potentially contributes to the bladder outlet obstruction (BOO) in women. METHODS: We reviewed the charts of 1367 women who underwent an urodynamic study. We included patients with a non-invasive free-flow study (NIFFS) and pressure flow study (PFS) performed through a 6 Fr double lumen transurethral catheter. RESULTS: In total, 120 women met the inclusion/exclusion criteria. Mean maximal flow rate (Qmax) was significantly higher (p < 0.001) in the NIFFS (27.2±11.1 mL/s) than in the PFS (19.3±10.6 mL/s). The mean difference between both Qmax was 7.9±12.0 mL/s. Of these women, 92.3% (24/26) with a Qmax <12 mL/s during PFS were found to have a Qmax ≥12 mL/s during the NIFFS. Ten of the 72 women with an available Pdet.Qmax were deemed to have a BOO according to the PFS and all of them had a Qmax >12 mL/s during the NIFFS. Of the 10 patients, only 2 reported obstructive symptoms. CONCLUSION: The presence of 6 Fr transurethral catheters alters the PFS and results in a significant reduction of the Qmax in patients who voided more than 250 mL. We believe that NIFFS should be performed in all patients before any urethral manipulation to lower a possible overdiagnosis of BOO and findings should always be correlated to clinical symptoms.

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)0.001
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 score0.019
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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

Citations11
Published2013
Admission routes2
Has abstractyes

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