Efficacy and Safety of COVID-19 Vaccines: A Systematic Review and Meta-Analysis of Randomized Clinical Trials
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- Metaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
- Consensus categories
- Metaresearch, Meta-epidemiology (broad)
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Meta-analysisConsensus signal: none
- Genre
- Candidate signal: ReviewConsensus signal: Review
- Teacher disagreement score
- 0.628
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.079 | 0.013 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.189 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
The current study systematically reviewed, summarized and meta-analyzed the clinical features of the vaccines in clinical trials to provide a better estimate of their efficacy, side effects and immunogenicity. All relevant publications were systematically searched and collected from major databases up to 12 March 2021. A total of 25 RCTs (123 datasets), 58,889 cases that received the COVID-19 vaccine and 46,638 controls who received placebo were included in the meta-analysis. In total, mRNA-based and adenovirus-vectored COVID-19 vaccines had 94.6% (95% CI 0.936-0.954) and 80.2% (95% CI 0.56-0.93) efficacy in phase II/III RCTs, respectively. Efficacy of the adenovirus-vectored vaccine after the first (97.6%; 95% CI 0.939-0.997) and second (98.2%; 95% CI 0.980-0.984) doses was the highest against receptor-binding domain (RBD) antigen after 3 weeks of injections. The mRNA-based vaccines had the highest level of side effects reported except for diarrhea and arthralgia. Aluminum-adjuvanted vaccines had the lowest systemic and local side effects between vaccines' adjuvant or without adjuvant, except for injection site redness. The adenovirus-vectored and mRNA-based vaccines for COVID-19 showed the highest efficacy after first and second doses, respectively. The mRNA-based vaccines had higher side effects. Remarkably few experienced extreme adverse effects and all stimulated robust immune responses.
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.
The record
- Venue
- Vaccines
- Topic
- SARS-CoV-2 and COVID-19 Research
- Field
- Medicine
- Canadian institutions
- University of Calgary
- Funders
- not available
- Keywords
- MedicineImmunogenicityAdjuvantAdverse effectPlaceboInternal medicineRandomized controlled trialCoronavirus disease 2019 (COVID-19)Clinical trialMeta-analysisImmune systemImmunologyPathology
- Has abstract in OpenAlex
- yes