An Interprofessional Approach in Caring for a Patient on Maintenance Hemodialysis with COVID-19 in Toronto, Canada: An Educational Case Report
Bibliographic record
Abstract
RATIONALE: Hemodialysis patients are at significant risk from COVID-19 due to their frequent interaction with the health care system and medical comorbidities. We followed up the trajectory of the first COVID-19-positive maintenance hemodialysis patient at Sunnybrook Health Sciences Centre in Toronto. We present the lessons learned and changes in practices that occurred to prevent an outbreak in our center. PRESENTING CONCERNS OF THE PATIENT: The patient, a 66-year-old woman on in-center hemodialysis, initially presented with a 2-day history of a productive cough. She subsequently developed a fever, was placed on contact and droplet isolation, and admitted to hospital. DIAGNOSES: On March 13, 2020, the patient tested positive for COVID-19. Within the next 48 hours, she developed hypoxia and acute respiratory distress syndrome as a complication of her illness requiring an extended critical care stay. This extended critical care stay resulted in critical illness-associated secondary sclerosing cholangitis. INTERVENTIONS: An interprofessional team was established, performing rapid Plan-Do-Study-Act quality improvement cycles to improve screening practices and promote the safety of patients and staff in the hemodialysis unit. OUTCOMES: We present here the lessons learned, the changes to our screening protocols, and the clinical course of our first in-center hemodialysis patient with SARS-CoV-2. TEACHING POINTS: Regular review of the infection screening processes is paramount in preventing outbreaks of COVID-19, particularly in hemodialysis units. Hospital admission should be arranged if a patient exhibits any clinical signs of hemodynamic compromise or hypoxia. Early education for health care practitioners caring for patients with COVID-19 and refresher information regarding personal protective equipment helped promote the safety of staff and prevent health care-associated outbreaks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".