The impact of <scp>COVID</scp>‐19 in hemodialysis patients: Experience in a hospital dialysis unit
Bibliographic record
Abstract
INTRODUCTION: COVID-19 is a very high transmission disease with a variable prognosis in the general population. Patients in hemodialysis therapy are particularly vulnerable to developing an infectious disease, but the incidence and prognosis of hemodialysis patients with COVID-19 is still unclear. The main objective is to describe the experience of our dialysis unit in preventing and controlling the spread of SARS-CoV-2 infection. METHODS: Preventive structural and organizational changes were applied to all patients and health care personnel in order to limit the risk of local transmission of SARS-CoV-2 infection. FINDINGS: The Nephrology department at Sant Joan Despí Moises Broggi Hospital-Consorci Sanitari Integral is a reference for two satellite hemodialysis centers caring for 156 patients. We combine our own hemodialysis maintenance program for 87 patients with hospitalized patients from peripheral hemodialysis centers. In this area, the reported incident rate of COVID-19 in these peripherical hemodialysis centers was 9.5% to 19.9% and the death rate 25% to 30.5%. In our hemodialysis program, the incidence rate was 5.7%. Three out of five required hospitalization (60%) and nobody died. DISCUSSION: Although the risk of local transmission of the disease was very high due to the increase in hemodialysis patients from peripheral centers admitted to hospital, the incidence rate of COVID-19 was very low in our own hemodialysis patients. We believe that the structural and organizational changes adopted early on and the diagnosis algorithm played an important role in minimizing the spread of the disease.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".