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Record W2808655725 · doi:10.2196/resprot.9654

An eHealth Program for Patients Undergoing a Total Hip Arthroplasty: Protocol for a Randomized Controlled Trial

2018· article· en· W2808655725 on OpenAlexvenueno aff
Rosemary Saunders, Karla Seaman, Catherine Ashford, Trudy Sullivan, Judith McDowall, Lisa Whitehead, Beverley Ewens, Kathryn Pedler, Karen Gullick

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
FundersRamsay Health Care
KeywordseHealthRandomized controlled trialProtocol (science)MedicineArthroplastyPhysical therapyTotal hip arthroplastyHip arthroplastyMedical physicsHealth careSurgeryAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Total hip arthroplasty is an effective surgical procedure commonly used worldwide for patients suffering the disabling effects of osteoarthritis when medical therapy is unsuccessful. Traditionally pre- and postoperative information for patients undergoing a hip arthroplasty has been provided by paper-based methods. Electronic health (eHealth) programs to support individualized patient education on preoperative preparation, in-patient care, and home rehabilitation have the potential to increase patient engagement, enhance patient recovery, and reduce potential postoperative complications. OBJECTIVE: The aim of this study is to compare the addition of an eHealth program versus standard care for pre- and postoperative education on patient outcomes for primary total hip arthroplasty. METHODS: One hundred patients undergoing a primary elective total hip arthroplasty will be recruited from a metropolitan hospital in Western Australia to participate in a 6-month parallel randomized control trial. Participants will be randomized to either the standard care group (n=50) and will be given the education booklet and enrolled to attend a 1-hour education session, or the intervention group (n=50), and will receive the same as the standard care plus access to an eHealth program titled "My Hip Journey." The eHealth program encourages the patient to log in daily, from 2 weeks prior to surgery to 30 days postsurgery. The information on the platform will be aligned with the patient's individual surgical journey and will include exercises to be completed each day for the duration of the program. The primary outcome measure is the Hip Dysfunction and Osteoarthritis Outcome Score, version LK 2.0. Secondary outcome measures include the EuroQoL EQ-5D-5L, a 5-level 5-dimension quality of life measure, and the Self-Efficacy for Managing Chronic Disease Scale. Data will be collected at pre-admission (presurgery) and at 6 weeks, 3 months, and 6 months postsurgery. A patient satisfaction survey will be completed 6 weeks postsurgery and Web-based analytics will be collected 6 months postsurgery. A cost-effectiveness analysis, using the intention-to-treat principle, will be conducted from the hospital's perspective. RESULTS: Enrollment in the study commenced in January 2018 with recruitment due for completion towards the end of the year. The first results are expected to be submitted for publication in 2019. CONCLUSIONS: The outcomes and cost of using an eHealth program to support a patient's recovery from a hip arthroplasty will be compared with standard care in this study. If the eHealth program is found to be effective, further implementation across clinical practice could lead to improvement in patient outcomes and other surgical areas could be incorporated. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12617001433392; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373657&isReview=true (Archived by WebCite at http://www.webcitation.org/6yzoTuggx). REGISTERED REPORT IDENTIFIER: RR1-10.2196/9654.

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)0.030
metaresearch head score (Gemma)0.029
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.086
Threshold uncertainty score0.288

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.029
Meta-epidemiology (narrow)0.0070.003
Meta-epidemiology (broad)0.0130.006
Bibliometrics0.0030.004
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0040.002
Research integrity0.0080.009
Insufficient payload (model declined to judge)0.0860.011

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 head0.115
GPT teacher head0.537
Teacher spread0.422 · 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
GenreProtocol

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

Citations12
Published2018
Admission routes1
Has abstractyes

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