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Record W2996143663 · doi:10.5737/23688076294226231

Quality of life in men after total cystoprostatectomy: Perceptions of Tunisian patients

2019· article· en· W2996143663 on OpenAlexaffvenue
Asma Ben Hassine, Intissar Souli, Raoua Braiki, Rabeb Chouigui, Abbessi Amira, Hatem Laaroussi, Boutheina Mejri, M. Ladib, Adnen Hidoussi

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversity of OttawaUniversité Laval
Fundersnot available
KeywordsQuality of life (healthcare)MedicineCystoprostatectomySexual functionArabicStoma (medicine)PsychologyGerontologyPhysical therapyGeneral surgeryBladder cancerCystectomyCancerNursingInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Total cystoprostatectomy (TCP) causes many changes in the postoperative quality of life leading to psychological, physical, social and sexual repercussions that are difficult to manage. This study aims to describe the postoperative quality of life of elderly Tunisian men who had a TCP as a result of a bladder cancer. METHODS: A descriptive quantitative study was conducted with 40 cystoprostatectomized men. Data collection tools were the Stoma-quality of life (QOL) questionnaire of Prieto, Thorsen, and Juul (2005) translated and validated to the Arabic language, and the Arabic version of the International Index of Erectile Function (IIEF5) questionnaire validated by Shamloul, Ghanem and Abou-Zeid (2004). RESULTS: 77.5% of participants had a very low quality-of-life score. All dimensions of quality of life-body image, physical, psychological, family and social life, and sexuality-were affected. In addition, all participants have suffered from severe sexual impotence after surgery. CONCLUSION: Counselling pre and postoperatively needed to facilitate the postoperative transition and ensure a better quality of life related to the health of men with bladder cancer.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.016
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.015
GPT teacher head0.320
Teacher spread0.304 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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