Mobile Web-Based Follow-up for Postoperative ACL Reconstruction: A Single-Center Experience
Bibliographic record
Abstract
BACKGROUND: The initial 6 weeks after surgery has been identified as an area for improvement in patient care. During this period, the persistence of symptoms that go unchecked can lead to unscheduled emergency room and clinic visits, calls to surgeons' offices, and readmissions. PURPOSE: To analyze postoperative data from a previous study examining postoperative outcomes in 2 patient populations following breast reconstruction and anterior cruciate ligament (ACL) reconstruction with use of a patient-centered mobile application. Here, the authors establish whether this method of follow-up can provide useful insight specific to the orthopaedic patient population, and they determine whether the mobile platform has the potential to modify their postoperative treatment. In addition, the authors examine its utility for orthopaedic physicians and patients. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Eligible patients undergoing ACL reconstruction from 2 surgeons were consecutively recruited to use a mobile smartphone application that allowed physicians to monitor their recovery at home. Data from 32 patients were collected via the application and analyzed to evaluate recovery trends during the first 6 postoperative weeks. Following completion of the study, patients and physicians were interviewed on their experience. RESULTS: Data collected from each question in the mobile application provided insightful trends on daily real-time indicators of postoperative recovery. The application identified 1 patient who required in-person reassessment to rule out a possible infection, following surgeon review of an uploaded image. It was estimated that the majority of patients could have avoided follow-up at 2 and 6 weeks, owing to the application's efficacy. Participants described their satisfaction with the device as excellent (43%), good (40%), fair (10%), and poor (7%), and 94% (n = 30) of patients reported that they would respond to questions using a similar application in the future. Both physicians rated their experience as positive and identified useful traits in the web portal. CONCLUSION: This system can accurately assess patient recovery; it has the potential to change how postoperative orthopaedic patients are followed, and it is well received by patients and physicians. Recognition of the study's limitations and employment of user feedback to improve the current application are essential before a formal randomized controlled trial is conducted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".