Implementation of Strategies for Prevention and Control of SARS-CoV-2 Infection at Dialysis Units in Latin America: Analysis from GlomCon Latin America Working Group (LGlomCon)
Bibliographic record
Abstract
Background: Patients on dialysis belong to the high-risk group to develop severe COVID-19 infection due to their multiple comorbidities. International societies have issued recommendations for the control and prevention of SARS-CoV-2 infection at dialysis units but implementing them may not always be feasible as many healthcare systems in Latin America (LA) have limited resources. This study aims to reflect the experience of nephrologists in LA at taking care of these patients and if the recommendations were adopted in their practices. Methods: Descriptive analysis extracted from an online survey carried out among nephrologists, renal pathologists and other health workers treating kidney diseases between May 20-27, 2020 from sixteen Spanish speaking LA countries divided into 6 categories. We present the results for the ESRD category. Results: 430 responses were obtained, 360 were considered for analysis. 276 (86.5%) of the participants were nephrologists and 178 (64%) of them practiced in dialysis units. 163 (92.6%) already implemented strategies to control and prevent COVID-19 in their units. 125 (71%) received training on it and 128 (72.7%) reported personal protective equipment availability. The most common implemented strategies were: education sessions about COVID-19 for patients and caregivers (68.5%), designated isolation areas (77.8%) or shifts (68.75%) for patients with suspected or confirmed COVID-19 and a 7-feet separation between hemodialysis (HD) machines (61.9%). 49 (28%) of the nephrologists reported an outbreak among patients and 60 (34.2%) among medical staff. Patient absenteeism to their HD sessions due to fear of infection, a decrease in the frequency and a shortening of the time of the sessions was reported in 41.7%, 30.2% and 36%, respectively. 29 (16.5%) of the respondents considered that those practices were associated with patient mortality. Conclusions: Most dialysis units in LA are partially implementing the recommended strategies for control and prevention of COVID-19 but this seems to be insufficient since at least one third of them already faced outbreaks among patients and medical staff.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".