Using Text Messaging in Long-Term Arthroplasty Follow-Up: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) and mobile technology have the potential to change the way patients are monitored following joint replacement surgery. OBJECTIVE: The aim of this study was to determine the feasibility of text messaging to record PROMs in long-term follow-up of hip and knee arthroplasty. Our participants were 17 patients 2-years-plus post hip or knee arthroplasty attending clinic with a mobile telephone number on record. METHODS: A simple PROM (Oswestry Very Short Form) was texted to the patient. Responses were compared to clinical, radiographic, and existing PROM findings. Patients were interviewed to discover their opinions on this use of texting. RESULTS: A total of 11 patients engaged with the text messaging. Reasons for not engaging included wrong numbers, physical barriers, and lack of understanding. A total of 8 patients attending clinic allowed comparison of text messaging with clinical findings. The average age was 70 years. A total of 4 patient text messaging responses matched clinical and radiographic findings; 3 also matched PROM scores collected in clinic. The 3 patients with mixed responses had abnormal clinical, radiographic, or PROM findings. One patient's text responses conflicted with clinical outcome. Analysis of patients' views showed a generally positive opinion: patients were happy to communicate with surgeons by text. Practical problems, PROM limitations, and trustworthiness of texting were highlighted. CONCLUSIONS: Engaging with changing technology creates challenges for patients and health care professionals. Despite this, our results suggest text messaging is a promising way to communicate with arthroplasty patients. Earlier integration of text communication in the patient pathway may be important and needs further research.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".