Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,006 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».