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Record W2080197111 · doi:10.1016/j.juro.2012.02.2443

2265 DEBILITATING LOWER URINARY TRACT SYMPTOMS IN THE POST-RENAL TRANSPLANT POPULATION CAN BE PREDICTED PRE-TRANSPLANTATION

2012· article· en· W2080197111 on OpenAlexaboutno aff
Marie Dion, Octav Cristea, Sarah Langford, Patrick Luke, Alp Şener

Bibliographic record

VenueThe Journal of Urology · 2012
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTransplantationPopulationUrologyUrinary systemKidney transplantationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyTransplantation & Vascular Surgery: Renal Transplantation, Vascular Surgery II1 Apr 20122265 DEBILITATING LOWER URINARY TRACT SYMPTOMS IN THE POST-RENAL TRANSPLANT POPULATION CAN BE PREDICTED PRE-TRANSPLANTATION Marie Dion, Octav Cristea, Sarah Langford, Patrick Luke, and Alp Sener Marie DionMarie Dion London, Canada More articles by this author , Octav CristeaOctav Cristea London, Canada More articles by this author , Sarah LangfordSarah Langford London, Canada More articles by this author , Patrick LukePatrick Luke London, Canada More articles by this author , and Alp SenerAlp Sener London, Canada More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.2443AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Renal transplantation (TX) is performed with increasing frequency in older patients who may have occult overactive bladder (OAB) or benign prostatic hyperplasia (BPH) masked by the low urine output of end-stage renal disease (ESRD). Sequelae of these disease processes may pose an underlying risk to renal allografts from episodes of urinary retention, infection, or increased collecting system pressure. The purpose of our study was to determine the frequency and severity of LUTS in the post-renal transplantation population and to determine if the use of pre-transplantation validated questionnaires could predict which patients will develop severe LUTS post-renal transplantation. METHODS All adult TX recipients between 2005 and 2010 were invited to participate in this study via mailed questionnaires. The Overactive Bladder Questionnaire (OAB-Q) was completed based on patient symptoms at three time points: pre-TX, and 6 and 12 months post-TX. Male patients also received the International Prostate Symptom Score sheet (IPSS) and were asked to complete the survey for the same three time points. Overall scores were tabulated based on the returned surveys. RESULTS Of the 465 patients who underwent renal TX, 105 patients participated in the study (response rate 22.6%). Pre-TX LUTS were common at 15% using the OAB-Q. Post-TX, 31% and 23% had moderate to severe symptoms at 6 and 12 months respectively. Health-related quality of life (HRQL) scores pre-TX were predictive of moderate to severe symptoms post-TX with an odds ratio of 11.2 (95% CI 2.7-45.9, p = 0.0012) at 6 months and 9.2 (95% CI 2.0–41.8, p = 0.0085) at 12 months. In male patients the IPSS found 40.8% of men had moderate to severe BPH symptoms pre-TX. When their post-TX symptoms were examined these patients were 9.4 times as likely to suffer moderate to severe symptoms as compared to patients with low IPSS scores at 12 months (95% CI 1.7-51.9, p = 0.0086). The quartile of patients with the most severe OAB-Q subscale scores post-TX was examined. Symptom severity, coping, and sleep components were found to be significantly worse post-TX in this group (p < 0.02). CONCLUSIONS Validated LUTS questionnaires prior to renal TX may predict patients who will suffer significant LUTS post renal-TX. Identification of patients at risk for LUTS could allow for screening of inappropriate TX candidates, and treatment of urologic symptoms in the pre-operative or early post-operative period via medical and or surgical interventions avoiding complications which could compromise renal allografts. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e914 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Marie Dion London, Canada More articles by this author Octav Cristea London, Canada More articles by this author Sarah Langford London, Canada More articles by this author Patrick Luke London, Canada More articles by this author Alp Sener London, Canada More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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)0.004
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.012
GPT teacher head0.270
Teacher spread0.259 · 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".

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Citations1
Published2012
Admission routes1
Has abstractyes

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