Early Multidrug Outpatient Treatment of SARS-CoV-2 Infection (COVID-19) and Reduced Mortality Among Nursing Home Residents
Bibliographic record
Abstract
Abstract The outbreak of COVID-19 from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread all over the world with tremendous morbidity and mortality in the elderly. In-hospital treatment addresses the multifaceted nature of the illness including viral replication, cytokine storm, and endothelial injury with thrombosis. We identified nine reports of early treatment outcomes in COVID-19 nursing home patients. Multi-drug therapy including hydroxychloroquine with one or more anti-infectives, corticosteroids, and antithrombotic agents can be extended to seniors in the nursing home setting without hospitalization. Data from nine studies found multidrug regimens relying on the use of hydroxychloroquine as well as other agents including doxycycline were associated with a statistically significant and >60% reductions in mortality. Going forward, we theorize and based on the evidence, that early empiric treatment for the elderly with COVID-19 in the nursing home setting (or similar congregated settings with elderly residents/patients) has a genuine probability of success and acceptable safety. This group remains our highest at-risk group and warrants acute treatment focus that will prevent the development and/or worsening of problems associated with COVID-19, most particularly isolation, hospitalization, and death. In fact, with the rapidity and severity of SARS-CoV-2 outbreaks in nursing homes, in-center treatment of patients with acute COVID-19 is possibly the most rational and importantly feasible strategy to reduce the risks of hospitalization and death. If the approach remains ‘wait-and-see’ and elderly high-risk patients in such congregated nursing room type settings are allowed to worsen with no early treatment, they may be too sick and fragile to benefit from in-hospital therapeutics and are at risk for pulmonary failure, life-ending micro-thrombi of the lungs, kidneys etc. We put forth the notion that the most important factor in this regard, is making available early therapeutic intervention as described here. These drugs include and under supervision by skilled doctors, combination/sequenced ivermectin, hydroxychloroquine, colchicine, azithromycin, doxycycline, bromhexine hydrochloride, and favipiravir (outside the US), along with inhaled steroids such as budesonide and oral steroids including dexamethasone and prednisone, and anti-thrombotic anti-clotting drugs such as heparin). As the clinical trials data on treatments for COVID-19 mature, this early treatment therapeutic option deserves serious, urgent, and sober consideration by the medical establishment and respective decision-makers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".