Enhanced Recovery 2.0 – Same Day Discharge With Mobile App Follow-up After Minimally Invasive Colorectal Surgery
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To investigate the feasibility of SDD protocol with postdischarge follow-up using a mobile phone app in patients undergoing elective minimally-invasive colectomy. SUMMARY OF BACKGROUND DATA: Discharge before gastrointestinal recovery and use of mobile health technology for remote follow-up may allow for SDD after minimally-invasive colectomy within an ERP. METHODS: Adult patients undergoing elective laparoscopic colectomy or loop ileostomy reversal from February 2020 to November 2020 were screened for eligibility. Patients were eligible if they lived within a 30-minute drive from the hospital, had an adequate support system at home, and owned a smart phone. Patients were discharged from the recovery room on the day of surgery based on set criteria with postdischarge remote follow-up using a mobile application. Feasibility was defined as discharge on the day of surgery without emergency department (ED) visit or readmission within the first 3 days. 30-day complications, ED visits, and readmissions were compared to a non-SDD historical cohort (May 2019-March 2020) also remotely followed-up using the same mobile phone app (standard ERP group). RESULTS: A total of 48 patients were recruited to SDD, of which 77% were discharged on the day of surgery without subsequent ED visit in the first 72 hours. There were 11 patients that could not be discharged, including 7 for failure of discharge criteria and 4 for intraoperative complications/concerns. Overall 30-day complications in the SDD group (17%) was similar to the standard ERP group (15%, P = 0.813). ED visits (SDD10% vs standard ERP8%, P = 0.664) and readmissions (6% vs 4%, P = 0.681) were also similar. CONCLUSIONS AND RELEVANCE: Findings from this study support the feasibility of a SDD protocol in select patients undergoing minimally-invasive colorectal resection. SDD colectomy protocols may represent the next evolution of ERP and postoperative recovery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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