Post Discharge after Surgery Virtual Care with Remote Automated Monitoring Technology (PVC-RAM): protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: After nonelective (i.e., semiurgent, urgent and emergent) surgeries, patients discharged from hospitals are at risk of readmissions, emergency department visits or death. During the coronavirus disease 2019 (COVID-19) pandemic, we are undertaking the Post Discharge after Surgery Virtual Care with Remote Automated Monitoring Technology (PVC-RAM) trial to determine if virtual care with remote automated monitoring (RAM) compared with standard care will increase the number of days adult patients remain alive at home after being discharged following nonelective surgery. METHODS: We are conducting a randomized controlled trial in which 900 adults who are being discharged after nonelective surgery from 8 Canadian hospitals are randomly assigned to receive virtual care with RAM or standard care. Outcome adjudicators are masked to group allocations. Patients in the experimental group learn how to use the study's tablet computer and RAM technology, which will measure their vital signs. For 30 days, patients take daily biophysical measurements and complete a recovery survey. Patients interact with nurses via the cellular modem-enabled tablet, who escalate care to preassigned and available physicians if RAM measurements exceed predetermined thresholds, patients report symptoms, a medication error is identified or the nurses have concerns they cannot resolve. The primary outcome is number of days alive at home during the 30 days after randomization. INTERPRETATION: This trial will inform management of patients after discharge following surgery in the COVID-19 pandemic and offer insights for management of patients who undergo nonelective surgery in a nonpandemic setting. Knowledge dissemination will be supported through an online multimedia resource centre, policy briefs, presentations, peer-reviewed journal publications and media engagement. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT04344665.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".