The Effect of Mobile App Follow-up Care on the Number of In-person Visits Following Ambulatory Surgery: A Randomized Control Trial
Bibliographic record
Abstract
Women's College Hospital (WCH) in Toronto offers specialized ambulatory surgical procedures. A feasibility study using a mobile appliciation (app) to supplement in-person follow-up care after surgery suggests that the mobile app adequately detects postoperative complications, eliminates the need for in-person follow-up care and is cost-effective. This is concordant with other postoperative telemedicine studies. The purpose of this study is to determine if we can avert in-person follow-up care through the use of mobile app compared to conventional, in-person follow-up care in the first month following surgery amongst breast reconstruction patients at WCH. This will be a pragmatic, single-centre, open, controlled, 2-arm parallel-group superiority randomized trial. Mobile app follow-up care is a novel approach to managing patients postoperatively with the potential to avert in-person follow-up and generate cost-savings for the healthcare system and patient.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 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".