Efficacy of COVID-19 treatments among geriatric patients: a systematic review
Bibliographic record
Abstract
Introduction: A majority of the fatalities due to COVID-19 have been observed in those over the age of 60. There is no approved and universally accepted treatment for geriatric patients. The aim of this review is to assess the current literature on efficacy of COVID-19 treatments in geriatric populations. Methods: A systematic review search was conducted in PubMed, MedRxiv, and JAMA databases with the keywords COVID-19, geriatric, hydroxychloroquine, dexamethasone, budesonide, remdesivir, favipiravir, ritonavir, molnupiravir, tocilizumab, bamlanivimab, baricitinib, sotrovimab, fluvoxamine, convalescent plasma, prone position, or anticoagulation. Articles published from January 2019 to January 2022 with a population greater than or equal to 60 years of age were included. Interventions examined included hydroxychloroquine, remdesivir, favipiravir, dexamethasone, budesonide, tocilizumab, bamlanivimab, baricitinib, sotrovimab, convalescent plasma, prone position, and anticoagulation therapy. Outcome measures included viral load, viral markers, ventilator-free days, or clinical improvement. Results: The search revealed 302 articles, 52 met inclusion criteria. Hydroxychloroquine, dexamethasone, and remdesivir revealed greater side effects or inefficiency in geriatric patients with COVID-19. Favipiravir, bamlanivimab, baricitinib, and supportive therapy showed a decrease in viral load and improvement of clinical symptoms. There is conflicting evidence with tocilizumab, convalescent plasma, and anticoagulant therapy in reducing mortality, ventilator-free days, and clinical improvements. In addition, there was limited evidence and lack of data due to ongoing trials for treatments with sotrovimab and budesonide. Conclusion: No agent is known to be effective for preventing COVID-19 after exposure to the virus. Further research is needed to ensure safety and efficacy of each of the reviewed interventions for older adults.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".