Therapeutic interventions for COVID-19: a living overview of reviews
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, but safe and effective treatment options remain unavailable. Numerous systematic reviews of varying qualities have tried to summarize the evidence on the available therapeutic interventions for COVID-19. This overview of reviews aims to provide a succinct summary of the findings of systematic reviews on different pharmacological and non-pharmacological therapeutic interventions for COVID-19. METHODS: We searched PubMed, Embase, Google Scholar, Cochrane Database of Systematic Reviews, and WHO database of publications on COVID-19 from 1 December 2019 through to 11 June 2020 for peer-reviewed systematic review studies that reported on potential pharmacological or non-pharmacological therapies for COVID-19. Quality assessment was completed using A MeaSurement Tool to Assess systematic Reviews-2 (AMSTAR-2) measure. RESULTS: Out of 816 non-duplicate studies, 45 were included in the overview. Antiviral and antibiotic agents, corticosteroids, and anti-malarial agents were the most common drug classes used to treat COVID-19; however, there was no direct or strong evidence to support their efficacy. Oxygen therapy and ventilatory support was the most common non-pharmacological supportive care. The quality of most of the included reviews was rated as low or critically low. CONCLUSION: This overview of reviews demonstrates that although some therapeutic interventions may be beneficial to specific subgroups of COVID-19 patients, the available data are insufficient to strongly recommend any particular treatment option to be used at a population level. Future systematic reviews on COVID-19 treatments should adhere to the recommended systematic review methodologies and ensure that promptness and comprehensiveness are balanced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".