COVID-19, children and non-communicable diseases: translating evidence into action
Bibliographic record
Abstract
The world faces an existential, once in a lifetime pandemic due to a novel coronavirus (SARS-CoV-2) which has to date infected over 25 million people across the world, with nearly 850 000 deaths.1 The disease, labelled COVID-19 by the WHO, has now spread to almost all the countries of the world and crippled the global economy. While high-income countries have been able to tap into their resources and reserves, for many low-income and middle-income countries, rising unemployment, population lock downs and closure of businesses have inflicted crippling damage on fragile economies, with rising inequalities and worsening poverty. While early reports of the infection2 3 suggested that the infection may be generally mild in children with COVID-19, with general case fatality rate less than 1%, there are increasing reports of complications among children and adolescents.4 In addition, a recent series of cases with multisystem inflammatory response merits reconsideration of these risks.5 There are also clear signals of predictors for adverse outcomes from COVID-19 infections. The disease has disproportionately taken a toll among the elderly population in long-term care facilities, with many dying without even being tested for COVID-19 infection.6 There is clear evidence of excess mortality in subgroups, especially those with comorbidities, most commonly related to non-communicable diseases (NCDs), such as diabetes, hypertension, obesity, heart disease and cancer.7 The same appears to be true among paediatric COVID-19 infections. A systematic review analysed a total of 7780 paediatric COVID-19 positive cases globally, and found that patients with information on underlying conditions (n=655) included the following comorbidities: immunosuppression (30.5%), respiratory conditions (20%) and cardiovascular disease (14%).8 A recent report from the UK of 651 hospitalised children with COVID-19 from 260 hospitals identified comorbidities in 42% (276/651) of cases.9 Comorbidities most commonly associated with …
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".