Using a Mobile App for Monitoring Post-Operative Quality of Recovery of Patients at Home: A Feasibility Study
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Bibliographic record
Abstract
BACKGROUND: Mobile apps are being viewed as a new solution for post-operative monitoring of surgical patients. Mobile phone monitoring of patients in the post-operative period can allow expedited discharge and may allow early detection of complications. OBJECTIVE: The objective of the current study was to assess the feasibility of using a mobile app for the monitoring of post-operative quality of recovery at home following surgery in an ambulatory setting. METHODS: We enrolled 65 consecutive patients (n=33, breast reconstruction surgery; n=32, orthopedic surgery) and asked them to use a mobile phone daily to complete a validated quality of recovery scale (QoR-9) and take photographs of the surgical site for the first 30 days post-op. Surgeons were asked to review patient-entered data on each patient in their roster daily. A semistructured questionnaire was administered to patients and surgeons to assess satisfaction and feasibility of the mobile device. RESULTS: All 65 patients completed the study. The mean number of logins was 23.9 (range 7-30) for the breast patients and 19.3 (range 5-30) for the orthopedic patients. The mean number of logins was higher in the first 14 days compared to the 15-30 days post-op for both breast patients (13.4 vs 10.5; P<.001) and for the orthopedic patients (13.4 vs 6.0; P<.001). The mean score for overall satisfaction with using the mobile device was 3.9 for breast patients and 3.7 for orthopedic patients (scored from 1 (poor) to 4 (excellent)). Surgeons reported on the easy-to-navigate design, the portability to monitor patients outside of hospital, and the ability of the technology to improve time efficiency. CONCLUSIONS: The use of mobile apps for monitoring the quality of recovery in post-operative patients at home was feasible and acceptable to patients and surgeons in the current study. Future large scale studies in varying patient populations are required.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it