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Record W2615531647 · doi:10.1177/2333721417709578

Incontinence and Nocturia in Older Adults After Hip Fracture: Analysis of a Secondary Outcome for a Parallel Group, Randomized Controlled Trial

2017· article· en· W2615531647 on OpenAlexafffund
Enav Z. Zusman, Megan M. McAllister, Peggy Chen, Pierre Guy, Heather Hanson, Khalil Merali, Penelope M. A. Brasher, Wendy L. Cook, Maureen C. Ashe

Bibliographic record

VenueGerontology and Geriatric Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsProvidence Health CareUniversity of CalgaryUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsNocturiaMedicinePhysical therapyRandomized controlled trialUrinary incontinenceHip fractureQuality of life (healthcare)OsteoporosisUrinary systemSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

Objective: To test the effect of a follow-up clinic on urinary incontinence (UI) and nocturia among older adults with hip fracture. Method: Fifty-three older adults (≥65 years) 3 to 12 months following hip fracture were enrolled and randomized to receive usual care plus the intervention (B4), or usual care (UC) only. The B4 group received management by health professionals, with need-based referrals. UI, nocturia, and quality of life were measured with questionnaires at baseline, 6 months, and 12 months. Results: There were 48 participants included in this analysis, and at baseline, 44% of study participants self-reported UI. At final assessment, six out of 24 B4 participants and 12 out of 24 UC participants reported UI. Four out of five study participants reported nocturia at baseline; this did not decrease during the study. Discussion: Following hip fracture, many older adults report UI and most report nocturia. Health professionals should be aware of the high occurrence of urinary symptoms among older adults post hip fracture.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.307
Teacher spread0.294 · 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 designRandomized trial
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

Citations10
Published2017
Admission routes2
Has abstractyes

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