Interventions for treatment of COVID-19: a protocol for a living systematic review with network meta-analysis including individual patient data (The LIVING Project)
Bibliographic record
Abstract
BACKGROUND: COVID-19 is a rapidly spreading virus infection that has quickly caused extensive burden to individual, families, countries, and the globe. No intervention has yet been proven effective for the treatment of COVID-19. Some randomized clinical trials assessing the effects of different drugs have been published, and more are currently underway. There is an urgent need for a living, dynamic systematic review that continuously evaluates the beneficial and harmful effects of all available interventions for COVID-19. METHODS/DESIGN: We will conduct a living systematic review based on searches of major medical databases (e.g., MEDLINE, EMBASE, CENTRAL) and clinical trial registries from their inception onwards to identify relevant randomized clinical trials. We will update the literature search once a week to continuously assess if new evidence is available. Two review authors will independently extract data and perform risk of bias assessment. We will include randomized clinical trials comparing any intervention for the treatment of COVID-19 (e.g., pharmacological interventions, fluid therapy, invasive or noninvasive ventilation, or similar interventions) with any comparator (e.g., an "active" comparator, standard care, placebo, no intervention, or "active placebo") for participants in all age groups with a diagnosis of COVID-19. Primary outcomes will be all-cause mortality and serious adverse events. Secondary outcomes will be admission to intensive care, mechanical ventilation, renal replacement therapy, quality of life, and non-serious adverse events. The living systematic review will include aggregate data meta-analyses, Trial Sequential Analyses, network meta-analysis, and individual patient data meta-analyses. Risk of bias will be assessed with domains, an eight-step procedure will be used to assess if the thresholds for clinical significance are crossed, and the certainty of the evidence will be assessed by Grading of Recommendations, Assessment, Development and Evaluations (GRADE). DISCUSSION: COVID-19 has become a pandemic with substantial mortality. A living systematic review evaluating the beneficial and harmful effects of pharmacological and other interventions is urgently needed. This review will continuously inform best practice in treatment and clinical research of this highly prevalent disease. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020178787.
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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 | 0.104 | 0.157 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.028 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.008 |
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".