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Record W4412165755 · doi:10.2196/preprints.78893

Correction: “Impact of Virtual Care With Remote Automated Monitoring on the Rate of Acute Hospital Care Post Discharge and Index Length of Hospital Stay: Protocol for the Post Discharge After Surgery Virtual Care With Remote Automated Monitoring Technology 3 (PVC-RAM-3) Trial” (Preprint)

2025· preprint· en· W4412165755 on OpenAlexaboutno aff
Sandra Ofori, Michael McGillion, Flávia K. Borges, Carley Ouellette, Ameen Patel, David Conen, Maura Marcucci, Michael Ke Wang, Lily Park, Conor Bell, Jennifer Lounsbury, Kanae Nagatani, Vikas Tandon, Trevor J Wilkieson, Ahraaz Wyne, Valerie Harvey, Rahima Nenshi, Jessica Bogach, John Harlock, Margherita Cadeddu, Shawn Forbes, Shariq Haider, Reza Mirza, Sunita Narang, Clare J. Reade, Daniel Tushinski, Amit Raut, Samir Raza, Ted Scott, Anthony Adili, Jeremy Petch, P.J. Devereaux

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsnot available
Fundersnot available
KeywordsIndex (typography)Protocol (science)Hospital dischargeMedicineEmergency medicineComputer scienceMedical emergencyIntensive care medicineWorld Wide WebAlternative medicine

Abstract

fetched live from OpenAlex

UNSTRUCTURED A substantial proportion of patients require acute hospital care after hospital discharge post surgery, and many regions and countries have surgical backlogs. The Post Discharge After Surgery Virtual Care with Remote Automated Monitoring Technology-3 (PCV-RAM-3) trial tests the hypothesis that informing surgeons and patients of virtual care with remote automated monitoring (VC-RAM) assignment will promote earlier discharge, thereby reducing the index length of hospital stay, and that postdischarge VC-RAM will reduce acute hospital care. The PVC-RAM-3 trial is a randomized controlled trial that compares VC-RAM to standard postdischarge care among 2500 adults undergoing elective noncardiac surgery in 3 Canadian hospitals. Following the randomization of patients prior to surgery, surgeons and patients are immediately notified whether the patient has been allocated to the VC-RAM or control group. Outcome adjudicators remain blinded to each participant’s group assignment. Patients in the intervention arm learn to use a Health Canada–approved cellular modem–enabled tablet computer and Bluetooth-enabled remote automated monitoring technology from Cloud DX to take daily wound photos for 7 days and measure daily vital signs (ie, blood pressure, heart rate, oxygen saturation, temperature, and weight) three times daily on days 1-7 and twice daily on days 8-14 post discharge, along with completing a brief recovery survey. Nurses review these data and conduct scheduled virtual visits (days 1, 3, 7, and 14). Nurses will escalate care to a preassigned and available perioperative care physician if predetermined vital sign thresholds are exceeded, concerning symptoms arise, or a medication error is detected. These physicians manage the issues and add or modify treatments as needed. The standard care group will receive postdischarge care as per the standard of care at the hospital where they undergo surgery. The coprimary outcomes are acute hospital care and the index hospital length of stay within the first 30 days after randomization. Study recruitment and follow-up are completed, and analysis of the study results is underway. This trial will offer insights into the role of VC-RAM in reducing acute hospital care and index length of hospital stay among adults undergoing elective surgery. ClinicalTrials.gov NCT05171569; https://clinicaltrials.gov/ct2/show/NCT05171569

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.258
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.236
Threshold uncertainty score0.791

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.258
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.2360.040

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.

Opus teacher head0.010
GPT teacher head0.305
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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