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 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 | 0.031 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".