Balancing the Needs of Acute and Maintenance Dialysis Patients during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic continues to strain health care systems and drive shortages in medical supplies and equipment around the world. Resource allocation in times of scarcity requires transparent, ethical frameworks to optimize decision making and reduce health care worker and patient distress. The complexity of allocating dialysis resources for both patients receiving acute and maintenance dialysis has not previously been addressed. Using a rapid, collaborative, and iterative process, BC Renal, a provincial network in Canada, engaged patients, doctors, ethicists, administrators, and nurses to develop a framework for addressing system capacity, communication challenges, and allocation decisions. The guiding ethical principles that underpin this framework are ( 1 ) maximizing benefits, ( 2 ) treating people fairly, ( 3 ) prioritizing the worst-off individuals, and ( 4 ) procedural justice. Algorithms to support resource allocation and triage of patients were tested using simulations, and the final framework was reviewed and endorsed by members of the provincial nephrology community. The unique aspects of this allocation framework are the consideration of two diverse patient groups who require dialysis (acute and maintenance), and the application of two allocation criteria (urgency and prognosis) to each group in a sequential matrix. We acknowledge the context of the Canadian health care system, and a universal payer in which this framework was developed. The intention is to promote fair decision making and to maintain an equitable reallocation of limited resources for a complex problem during a pandemic.
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 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".