Bibliographic record
Abstract
Dear Editor, This is an addition to the article titled “COVID Vaccine” is not the excuse to delay adaptation to the “New-Normal” by Deshmukh et al.[1] in your esteemed journal. As India is in due to the COVID-19 second wave crisis, the arrival of third-wave seems inevitable with this virus. When many national and international experts have already raised the possibility of a third wave of the pandemic, the Indian Institute of Public health, Bengaluru has warned the nation that the third wave will affect mostly the younger age group. Several countries have already witnessed the trailer of the third wave and started preparing to face it heads on. In our country, vaccination for older children with COVID-19 appropriate behaviors is among the measures that could help to prevent the third wave that is anticipated to be in October 2021. So, we suggest three key actions be taken to prevent our youth and children scum to the infection, which will be devastating. The first key action is either to speed up the vaccination or complete lockdown for a month. The latter is not advised considering the economic situation in our country. The second is to widen the age group for vaccination, i.e. to include adolescents and boost up the protected population.[2] The third is to pump up the vaccine production and intensively immunize the susceptible population between June and August by giving emergency approval to the existing viral vectors and killed vaccines. Having said this, we understand that vaccinating children against COVID is a complex issue considering the emotions of our people but there is a strong argument that adolescents could be given the jabs if the regulatory approach is granted. We feel our country will be in a much better position to completely lift all restrictions including removal masks after all adults have had gained at least some immunity from their first jab. In our country, we recommend the use of a single dose of Sputnik V vaccine to adolescents and adults while continuing the COVAXIN and COVISHIELD for older adults and senior citizens. India has a population of 598,993,990[3] constituting around 60% of the total population in the age group of 10 to 44 years. Currently, India is running vaccination drives for 45 years and above age group, which constitutes only 20% of the population. To prevent a third wave, 70% of this 10–44 age group, i.e. a population of 417,615,793 should get the vaccination before the predicted timeline for the third wave, which is October 2021. To compute, this will take at least 5 months to cover the desired 70% of the 10 to 44 age group. Currently, India’s daily vaccination coverage is around 300,000 doses all over the country.[4] Countries like the US, UK, and Canada have already planned to roll out adolescent vaccination to prevent the third wave. It’s time we all join hands with our policymakers to start thinking in these lines and chalk out plans and strategies to roll out adolescent and young adults vaccination without any delay to protect the nation. Financial support and sponsorship Nil Conflicts of interest Nil
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".