Conventional Follow‐up Versus Mobile Application Home Monitoring for Postoperative Anterior Cruciate Ligament Reconstruction Patients: A Randomized Controlled Trial
Bibliographic record
Abstract
PURPOSE: To determine whether a mobile app can reduce the need for in-person visits and examine the resulting societal cost differences between mobile and conventional follow-up for postoperative anterior cruciate ligament (ACL) reconstruction patients. METHODS: Study design was a single-center, 2-arm parallel group randomized controlled trial. All patients undergoing ACL reconstruction aged 16 to 70 years were screened for inclusion in the study. Competent use of a mobile device and ability to communicate in English was required. Patients were randomly assigned to receive follow-up via a mobile app or conventional appointments. Analysis was intention-to-treat. The primary outcome was the number of in-person visits to any health care professional during the first 6 postoperative weeks. Secondary outcomes included analysis of costs incurred by the health care system and personal patient costs related to both methods of follow-up. Patient-reported satisfaction and convenience scores, rates of complications, and clinical outcomes were also analyzed. RESULTS: Sixty patients were analyzed. Participants in the app group attended a mean of 0.36 in-person visits versus 2.44 in-person visits in the conventional group (95% confidence interval 0.08-0.28; P < .0001). On average, patients in the app group spent $211 (Canadian dollars) less than the conventional group over 6 weeks (P < .0001) on personal costs related to follow-up. Health care system costs were also significantly less in the app group ($157.5 vs CAD $202.2; P < .0001). There was no difference between groups in patient satisfaction, convenience, complication rates, or clinical outcome measures. CONCLUSIONS: Mobile follow-up can eliminate a significant number of in-person visits during the first 6 postoperative weeks in patients undergoing ACL reconstruction with cost savings to both the patient and health care system. This method should be considered for dissemination among similar orthopaedic procedures during early postoperative care. LEVEL OF EVIDENCE: I: Prospective randomized controlled trial.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".