Diagnosis for global health: Threats of infectious disease and why we continue to ignore them.
Bibliographic record
Abstract
In a matter of several weeks, an unknown infectious agent spread over the entire globe; it infected over 2.2 million people and killed at least 150,000 (as of April 2020). This infectious agent has been identified as a novel coronavirus and is now known as COVID-19. In 2007, Cheng et al. warned us of the reemerging ability of coronaviruses and the “ticking time bomb” that awaits. In 2008, Talbot et al. also warned that SARS was only the tip of the iceberg with regards to coronaviruses. There has been an endless amount of information available about how to prevent and minimize the risk of future outbreaks. We knew the importance of implementing these strategies 16 years ago; nonetheless, the number of people infected by COVID-19 are climbing each day. SARS was not the only infectious disease we could have learned from: a more recent example is Ebola. In 2015, Bill Gates discussed the impact of Ebola, warned that “if anything kills over 10 million people in the next few decades, it’s most likely to be a highly infectious virus rather than a war”, and made suggestions to invest in stronger public health systems. We had the time to implement these suggestions made over the years, yet they were not taken seriously and for the current pandemic, it is too late to use preventative measures as we are managing the consequences. Hopefully, in the world post-COVID-19, these important lessons will finally be applied and our global health system will become stronger.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".