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Urinary Retention in Patients in a Geriatric Rehabilitation Unit: Prevalence, Risk Factors, and Validity of Bladder Scan Evaluation

2001· article· en· W2116656426 on OpenAlexaff
Michael Borrie, Karen Campbell, Zora A. Arcese, Judy Bray, Pauline Hart, Terri Labate, Paul Hesch

Bibliographic record

VenueRehabilitation Nursing · 2001
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsParkwood InstituteLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsUrinary retentionRehabilitationGeriatric rehabilitationMedicineUnit (ring theory)Environmental healthUrinary systemGerontologyPhysical therapyPsychologyInternal medicineUrology

Abstract

fetched live from OpenAlex

The purpose of this study was to identify risk factors for urinary retention (UR) in frail, elderly patients, to determine its prevalence, and to assess the validity of the use of the BladderScan BVI 2500+ ultrasound scanner to measure postvoid residual urine volumes of > or = 150 ml. Probable UR was defined as two consecutive ultrasound scans with postvoid residual urine estimations of > or = 150 ml. The estimates were confirmed by in- and out-catheterization of actual postvoid residual urine (PVR). Risk factors for UR were the independent variables used in the regression analysis. Nineteen of the 167 people (11%) had UR. The risk of UR was greatest among patients who were older, or who were on anticholinergic medication, or who had diabetes of long standing, or who had fecal impaction. The correlation between paired scans and catheter volumes of > or = 150 ml was 0.87. The results suggest that the BladderScan BVI 2500+ ultrasound scanner, when used by trained nursing staff, provides conservative and valid estimates of PVR of > or = 150 ml in people undergoing geriatric rehabilitation.

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)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.347
Teacher spread0.309 · 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 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

Citations61
Published2001
Admission routes1
Has abstractyes

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