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Record W2938269220 · doi:10.1097/spv.0000000000000724

The Preferred Catheter Type After Prolapse Surgery: A Survey Study of Surgeons

2019· article· en· W2938269220 on OpenAlexaffabout
Anjali Kulkarni, Colleen D. McDermott

Bibliographic record

VenueFemale Pelvic Medicine & Reconstructive Surgery · 2019
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCatheterUrinary retentionIndwelling catheterUrinary catheterSurgeryPreference

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to determine surgeon preference for catheter type in the management of postoperative urinary retention after prolapse surgery, specifically comparing transurethral indwelling catheters (TIC), clean intermittent self-catheterization (CISC), and suprapubic tubes (SPT). METHODS: Electronic surveys were sent to 1182 urogynecologists and urologists through the American Urogynecologic Society and the Canadian Society of Pelvic Medicine. RESULTS: A total of 247 (21%) surveys were completed, where 53% of the respondents ranked TIC as the best catheter option, compared with 42% for CISC and 4% for SPT (P < 0.0001). Most (75%) of the respondents stated they do not offer their patients a choice in catheter selection. Most (43%) of the respondents ranked ease of use for the patient as the most important catheter characteristic. For ease of use for the patient, 71% of the respondents ranked TIC as the best, compared with CISC and SPT. For all other characteristics (pain/discomfort, infection, catheter malfunction, and return of bladder function), CISC was ranked as the best by the majority. CONCLUSIONS: This study showed that surgeons have a significant preference for TIC over CISC and SPT for the management of postoperative urinary retention, and the majority of surgeons do not offer their patients a choice with regard to catheter type.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.288
Teacher spread0.243 · 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 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

Citations6
Published2019
Admission routes2
Has abstractyes

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