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Record W1991274401 · doi:10.1097/aln.0b013e31819f7aea

Postoperative Urinary Retention

2009· review· en· W1991274401 on OpenAlexaff
Gabriele Baldini, Hema Bagry, Armen Aprikian, Franco Carli, David S. Warner, Mark A. Warner

Bibliographic record

VenueAnesthesiology · 2009
Typereview
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsMontreal General HospitalMcGill University Health Centre
Fundersnot available
KeywordsMedicineUrinary retentionUrinary systemPerioperativeIncidence (geometry)AnesthesiaUrinary catheterizationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Urinary retention is common after anesthesia and surgery, reported incidence of between 5% and 70%. Comorbidities, type of surgery, and type of anesthesia influence the development of postoperative urinary retention (POUR). The authors review the overall incidence and mechanisms of POUR associated with surgery, anesthesia and analgesia. Ultrasound has been shown to provide an accurate assessment of urinary bladder volume and a guide to the management of POUR. Recommendations for urinary catheterization in the perioperative setting vary widely, influenced by many factors, including surgical factors, type of anesthesia, comorbidities, local policies, and personal preferences. Inappropriate management of POUR may be responsible for bladder overdistension, urinary tract infection, and catheter-related complications. An evidence-based approach to prevention and management of POUR during the perioperative period is proposed.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.070
GPT teacher head0.355
Teacher spread0.285 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations825
Published2009
Admission routes1
Has abstractyes

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