The Hip Instructional Prehabilitation Program for Enhanced Recovery (HIPPER) as an eHealth Approach to Presurgical Hip Replacement Education: Protocol for a Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Osteoarthritis (OA), leading to hip replacement (THR), is a primary contributor to global mobility impairment. In 2018, more than 59,000 THR surgeries were performed in Canada. Health promotion education, such as prehabilitation, is vital to optimizing surgical outcomes. OBJECTIVE This study aims to evaluate the feasibility of the Hip Instructional Prehabilitation Program for Enhanced Recovery (HIPPER), an eHealth approach to prehabilitation education. METHODS A single-blind (assessor-blind), 2-arm, feasibility randomized controlled trial will be conducted. We will recruit 40 (HIPPER group, n=20; control group, n=20) older adults with hip OA and on a waitlist for a THR. The HIPPER intervention consists of 12 online, interactive modules. The control group will receive the current standard practice consisting of 2 online educational sessions lasting 2 hours each (webinars). Feasibility outcomes (eg, recruitment and retention rates) will be evaluated. RESULTS Recruitment started in March 2021. As of April 20, 2021, 18 participants were recruited. All 18 completed T1 measures. Only 1 participant has been scheduled to have a surgery and therefore has been scheduled to complete T2 measures. The remainder of the participants are waiting to be notified of their surgery date. This project was funded by a Canadian Institutes of Health Research Project Grant. Our institute’s research ethics board approved this study in November 2016. CONCLUSIONS Results will lead to refinement of the HIPPER protocol in order to evaluate a standardized and geographically accessible prehabilitation program. CLINICALTRIAL ClinicalTrials.gov NCT02969512; https://clinicaltrials.gov/ct2/show/NCT02969512 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/29322
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.035 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.106 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".