Optimising patient engagement for assessing total joint arthroplasty outcomes – a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Patient satisfaction is a critical outcome in total joint arthroplasty (TJA), yet assessing it effectively remains a challenge due to limitations in patient-reported outcome measures (PROMS). While these measures are commonly gathered in clinical settings, additional contact through mail or phone is often needed, and low response rates can affect the validity and reliability of collected data. To improve response rates, this study evaluated various methods of incentivizing patient participation in a randomized trial format, focusing on postal questionnaires. PATIENTS AND METHODS: The study investigated three methods to improve response rates: including a gift card with the questionnaire, promising a gift card upon questionnaire completion, and offering no incentive. It also examined whether different monetary values and the inclusion of the surgeon's name on materials impacted response rates. We tried to determine factors that could improve follow up telephone response rates in the group of patients that failed to return their questionnaires. RESULTS: Higher response rates were observed with monetary incentives (P = 0.056), larger amounts of money offered (P = 0.3839) for filling out the questionnaire, and if the surgeon's details were on the cover letter or questionnaire (P = 0.632). There was no correlation between age and sex and participation. We did find a statistically significant difference in total participation and poorer total knee arthroplasty outcomes scores (P < 0.001). CONCLUSION: Our study supports findings from prior research indicating that monetary incentives and personalized materials can improve response rates, although in this cohort, results were modest. Follow-up calls further boosted response rates, suggesting that multi-modal engagement may be beneficial. Although the response improvements were limited and lacked statistical significance, the study highlights the importance of refining strategies to ensure reliable PROMS data, which is vital for understanding patient outcomes in TJA. Future studies might consider demographic factors and other outreach methods to enhance PROMs data collection.
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 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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".