COVID-19 vaccine-hesitancy is associated with lower cortical volume in elderly individuals
Bibliographic record
Abstract
Background: According to a large number of scientific reports, the main problem is COVID-19 vaccine hesitancy which slowed down the vaccination program. Previous studies revealed that COVID-19 vaccine hesitancy is associated with lower cognitive performance. However, the neurobiology of such behavior is less known, and investigating the brain structural patterns in this regard can extend our knowledge on the basis of this behavior. This study aimed to investigate the link between brain structural features including cortical and subcortical volume with COVID-19 vaccine hesitancy in elderly individuals.Methods: A total of 221 healthy subjects without any cognitive impairment with a mean age of 63.7 ± 6.1 were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Overall, 87 vaccine-hesitant (VH) and 134 vaccine-accepted (VA) were entered into this study. The difference in the volume of cortical and subcortical regions was investigated between VH and VA groups.Results: There was no significant difference in cognitive status measured by MMSE, MoCA, ADAS-cog, and RAVLT between VA and VH groups (P>0.05). The analysis showed that VA subjects had significantly higher left pars orbitalis (P: 0.013), left precentral (P: 0.042), right caudal anterior cingulate (P: 0.044), and right isthmus cingulate (P: 0.013) volume compared to the VH group. There was no significant difference in other cortical and subcortical regions.Conclusion: In conclusion, this finding demonstrated that in the era of complicated decision-making due to social media reports, elderly adults with smaller frontal and cingulate regions are more likely to be vaccine-hesitant. These findings can highlight the link between cortical regions and health-protective behaviors such as taking up the offer of vaccination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".