BCG as a game-changer to prevent the infection and severity of COVID-19 pandemic?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The impact of COVID-19 is changing with country wise and depend on universal immunization policies. COVID-19 badly affects countries that did not have universal immunization policies or having them only for the selective population of countries (highly prominent population) like Italy, USA, UK, Netherland, etc. Universal immunization of BCG can provide great protection against the COVID-19 infection because the BCG vaccine gives broad protection against respiratory infections. BCG vaccine induces expressions of the gene that are involved in the antiviral innate immune response against viral infections with long-term maintenance of BCG vaccine-induced cellular immunity. COVID-19 cases are reported very much less in the countries with universal BCG vaccination policies such as India, Afghanistan, Nepal, Bhutan, Bangladesh, Israel, Japan, etc. as compared to without BCG implemented countries such as the USA, Italy, Spain, Canada, UK, etc. BCG vaccine provides protection for 50-60 years of immunization, so the elderly population needs to be revaccinated with BCG. Several countries started clinical trials of the BCG vaccine for health care workers and elderly people. BCG can be uses as a prophylactic treatment until the availability of the COVID-19 vaccine.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it