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Record W4214929774 · doi:10.5770/cgj.25.530

Safety and Tolerability of Fesoterodine in Older Adult Patients with Overactive Bladder

2022· review· en· W4214929774 on OpenAlexaffvenue
John Heesakkers, Manon te Dorsthorst, Adrian Wagg

Bibliographic record

VenueCanadian Geriatrics Journal · 2022
Typereview
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversity of Alberta
FundersLes Laboratories Pierre Fabre
KeywordsOveractive bladderMedicineTolerabilityAdverse effectIntensive care medicineComorbidityDosingPolypharmacyAnticholinergicInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

Background: Older patients (> 65 yr) suffering from overactive bladder (OAB) are more likely to have functional impairment and comorbidity than those without OAB. This article reviews available published studies and discusses how fesoterodine might meet the specific needs of the older OAB patient. Methods: A comprehensive literature search was undertaken in order to evaluate fesoterodine safety in older OAB patients. Results: Fesoterodine offers flexible dosing, allowing the clinician to balance risk and benefits according to the symptoms and preferences of the patient. Its balanced affinity for M2 and M3 muscarinic receptors may lead to its benefit on OAB symptoms. Its active metabolite is a P-gp substrate that is actively transported from the central nervous system (CNS), potentially avoiding adverse CNS effects. Fesoterodine can be used in mild or moderate hepatic or renal insufficiency and no dose adjustment is routinely required. Fesoterodine's benefit has been demonstrated in multiple clinical trials in older and medically vulnerable patients. Fesoterodine was rated as "beneficial" in the LUTS-FORTA classification due to its efficiency and tolerability in older patients. Conclusion: Here, the use of fesoterodine in older and vulnerable patients is summarized given the need to approach pharmacotherapy for OAB differently in older adults.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.024
GPT teacher head0.300
Teacher spread0.276 · 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 designOther design
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

Citations4
Published2022
Admission routes2
Has abstractyes

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