Pragmatic Recommendations for the Management of COVID-19 Patients with Shock in Low- and Middle-Income Countries
Bibliographic record
Abstract
As some patients infected with the novel coronavirus progress to critical illness, a subset will eventually develop shock. High-quality data on management of these patients are scarce, and further investigation will provide valuable information in the context of the pandemic. A group of experts identify a set of pragmatic recommendations for the care of patients with SARS-CoV-2 and shock in resource-limited environments. We define shock as life-threatening circulatory failure that results in inadequate tissue perfusion and cellular dysoxia/hypoxia, and suggest that it can be operationalized via clinical observations. We suggest a thorough evaluation for other potential causes of shock and suggest against indiscriminate testing for coinfections. We suggest the use of the quick Sequential Organ Failure Assessment (qSOFA) as a simple bedside prognostic score for COVID-19 patients and point-of-care ultrasound (POCUS) to evaluate the etiology of shock. Regarding fluid therapy for the treatment of COVID-19 patients with shock in low-middle-income countries, we favor balanced crystalloids and recommend using a conservative fluid strategy for resuscitation. Where available and not prohibited by cost, we recommend using norepinephrine, given its safety profile. We favor avoiding the routine use of central venous or arterial catheters, where availability and costs are strong considerations. We also recommend using low-dose corticosteroids in patients with refractory shock. In addressing targets of resuscitation, we recommend the use of simple bedside parameters such as capillary refill time and suggest that POCUS be used to assess the need for further fluid resuscitation, if available.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".