10: IMPACT OF A VIRTUAL ICU RECOVERY CLINIC IN SASKATCHEWAN, CANADA
Bibliographic record
Abstract
Introduction: Many ICU survivors struggle with post-intensive care syndrome (PICS) and chronic health issues post-discharge. In Saskatchewan, >50% of the population lives rurally, making follow-up care difficult. The Virtual ICU Recovery Clinic (VIRC) was piloted to provide comprehensive, multidisciplinary care to ICU survivors, using virtual care including telehealth and phone visits. Methods: The VIRC recruited ICU survivors in Regina, Saskatchewan from June 2022-June 2023. They were followed for 180 days post-discharge. The VIRC multidisciplinary team included a physician, pharmacist, and clinical psychologist. We evaluated emergency department (ED) visits, hospital, and ICU readmissions; health-related quality of life; clinic interventions; and satisfaction with the clinic. Results: As follow-up is still underway, we present an interim analysis. We recruited 68 patients, with a median age 61 years (IQR 50-70), clinical frailty score 4 (IQR 3-5), SOFA score admission 11 (IQR 8-12), ICU length of stay 6 days (IQR 4-13) and hospital length of stay 19 (IQR 10-43) days. 50% were female, 49% lived >50 km from Regina, and 78% had a family physician. Of the 33 patients who were seen in the clinic so far, nine (27%) have had ED visits, with five (15%) requiring hospital readmission and two (6%) requiring ICU readmission, by time of clinic visit. Median EQ-5D-5L and EQ-VAS scores at time of clinic visit were 0.84 (IQR 0.64-0.96) and 70 (IQR 55-75). Of those working prior to hospitalization, only 20% had returned to work by the time of the clinic visit. In terms of clinic interventions, 34% were determined to have PICS symptoms and were newly referred onto Clinical Psychology, 12% were newly referred to physiotherapy, and 76% were seen by the Clinical Pharmacist. Of the nine patients who completed all visits and consented to a post-clinic survey, 100% agreed/strongly agreed that the clinic was useful in their care, 78% agreed/strongly agreed that the clinic would keep them out of hospital and 71% agreed/strongly agreed that the virtual technology was easy to use. Conclusions: An ICU recovery clinic could potentially be delivered virtually, with potential impacts on post-ICU care. ICU survivors were in general satisfied with the provision of post-ICU care and the use of virtual technology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".