Current status of COVID‐19 among hemodialysis patients in the East Azerbaijan Province of Iran
Bibliographic record
Abstract
INTRODUCTION: Management of vulnerable patients during the COVID-19 pandemic requires careful precautions. Hemodialysis patients constitute a large group of at-risk patients that not only suffer from a compromised immune system but also are at a higher risk due to frequent admission to healthcare units. Therefore, a better understanding on the pathogenesis and possible risk factors of COVID-19 in hemodialysis patients is of high importance. METHODS: A total of 670 maintained hemodialysis patients from all dialysis units of the East Azerbaijan Province of Iran, including 44 COVID-19 patients were included in the present study. Possible associations between the backgrounds of patients and the incidence of COVID-19 were assessed. Also, hemodialysis patients with COVID-19 were compared to 211 nonhemodialysis COVID-19 patients. FINDINGS: and those with hypertension etiology of kidney failure demonstrated a lower incidence of COVID-19. Hemodialysis patients with COVID-19 had higher counts of polymorphonuclears (PMNs) in their peripheral blood compared to other COVID-19 patients. DISCUSSION: A better comprehension on the risk factors associated with COVID-19 in hemodialysis patients can improve our understanding on the pathogenesis of COVID-19 in different situations and help the enhancement of current therapeutics for COVID-19 in hemodialysis patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".