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Record W4313236352 · doi:10.1111/jep.13804

Impact of an online, individualised, patient reported outcome measures based patient decision aid on patient expectations, decisional regret, satisfaction, and health‐related quality‐of‐life for patients considering total knee arthroplasty: Results from a randomised controlled trial

2022· article· en· W4313236352 on OpenAlexaff
Deborah A. Marshall, Logan Trenaman, Karen V. MacDonald, Jeffrey Johnson, Dawn Stacey, Gillian Hawker, Christopher Smith, D'Arcy Durand, Nick Bansback

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2022
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsUniversity of TorontoUniversity of OttawaAlberta Bone and Joint Health InstituteCentre for Advancing Health OutcomesUniversity of British ColumbiaOttawa HospitalResearch CanadaUniversity of AlbertaUniversity of Calgary
FundersEuroQol Research Foundation
KeywordsMedicineRegretPhysical therapyQuality of life (healthcare)Logistic regressionArthroplastyPatient satisfactionPatient-reported outcomeDescriptive statisticsOsteoarthritisSurgeryNursingAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

RATIONALE: Total knee arthroplasty is a common surgical procedure but not appropriate for all patients with knee osteoarthritis. Patient decision aids (PtDAs) can promote shared decision making and enhance understanding and expectations of procedures among patients, resulting in better discussions between patients and healthcare providers about whether total knee arthroplasty is the most appropriate option. AIMS AND OBJECTIVES: Evaluate impact of an individualised PtDA for osteoarthritis patients considering total knee arthroplasty 1 year after baseline assessment. METHODS: Prospective, randomised controlled trial comparing an intervention arm (IA) and routine care arm (RCA). The IA included an online individualised patient reported outcome measures (PROMs) based PtDA and one-page summary report for the surgeon. We report secondary outcomes from the final assessment: patient expectations, decisional regret, patient satisfaction with outcomes of knee replacement, health-related quality-of-life (HRQOL) and depression. We report changes in HRQOL between baseline and final assessments, study arms, and surgical versus non-surgical patients. Descriptive statistics were used to describe participant characteristics and continuous variables. Dichotomous outcomes (expectations, decisional regret, satisfaction) were analyzed using logistic regression and continuous outcomes (HRQOL, depression) were modelled using linear regression. RESULTS: Overall, 140 participants completed all study assessments (IA: n = 69, RCA: n = 71); n = 108 underwent surgery (IA: n = 49, RCA: n = 59). Regardless of study arm, most participants reported expectations were met, minimal decisional regret, satisfaction with outcomes of knee replacement, and had improvements in HRQOL. While no significant differences in study outcomes were found between study arms, IA results were in the direction hypothesised in favour of the PtDA. CONCLUSIONS: Although we were not able to detect statistically significant benefits associated with implementing this PROMs-based PtDA, there was no apparent negative effect on these outcomes 1 year after baseline. We anticipate there may be benefit to implementing this PtDA earlier in the osteoarthritis care pathway where patients have more opportunities to manage their disease non-surgically.

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.193
GPT teacher head0.476
Teacher spread0.283 · 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 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".

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Citations18
Published2022
Admission routes1
Has abstractyes

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