Cost-effective Care Delivery Models in Ambulatory Surgery: Expedited Discharge and Virtual Care
Bibliographic record
Abstract
My Master’s thesis applies the Value Agenda framework to the ambulatory breast reconstruction population. The first study uses patient level quality of recovery (QoR) data to determine if autologous breast reconstruction is suitably performed in an ambulatory facility. We found that QoR scores approach baseline by postoperative day 7; and are comparable to other ambulatory surgery patient populations1. In keeping with the Value Agenda, this study uses patient outcome data to support autologous breast reconstruction in more cost-effective, ambulatory facilities. The second study models the cost-effectiveness of replacing in-person follow-up care with mobile app follow-up care during the first month following ambulatory breast reconstruction. We found a societal incremental net benefit of $245 CAD between mobile app and in-person follow-up care. Mobile app follow-up care expands the geographical reach of breast reconstruction by reducing the burden of physical patient travel and its associated costs after surgery.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".