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Record W4316372400 · doi:10.21037/asj-22-30

Addressing Global Disparities in Pediatric and Congenital Cardiac Care: introduction to the special series

2023· article· en· W4316372400 on OpenAlexaff
Dominique Vervoort, Marcelo Cardarelli

Bibliographic record

VenueAME Surgical Journal · 2023
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSeries (stratigraphy)MedicineIntensive care medicineBiology

Abstract

fetched live from OpenAlex

Over one million children are born with congenital heart disease (CHD) each year around the world (1).Approximately one in two children with CHD will require surgical or interventional care at least once in their lifetime (2), whereas one in five will need an intervention to survive to their first birthday (3).In addition, rheumatic heart disease (RHD) represents the most common acquired cardiovascular disease among children and adolescents (4).RHD is a disease of poverty largely eradicated in high-income countries, yet it continues to affect dozens of millions of people across low-and middle-income countries (LMICs) (4,5).Despite this considerable burden of cardiac surgical disease, over 90% of children in LMICs no access to cardiovascular care (6,7) resulting in more than 90% of deaths and disability-adjusted life-years (DALYs) in children to be "excess" (8).The current special series on "Addressing Global Disparities in Pediatric and Congenital Cardiac Care" provides a detailed overview of cardiovascular care for children living with cardiovascular diseases worldwide, with a particular emphasis on variable-resource contexts, where the disparities are greatest.Articles are briefly introduced in this editorial and include: (I) Addressing Global Disparities in Pediatric and Congenital Cardiac Care: introduction to the special series.(II) Narrative review in pediatric and congenital heart surgery in sub-Saharan Africa: challenges and opportunities in a new era.(III) "Regale una Vida" a successful social program for underprivileged children with congenital heart disease in a middle-income country.(IV) Pediatric cardiac NGOs: collaboration and coordination.(V) Fostering a sustainable pediatric cardiac workforce in the developing world during the current coronavirus disease 2019 (COVID-19) pandemic.(VI) Pediatric Cardiac Development Assistance in Conflict Zones.(VII) The road to regionalization in congenital heart surgery: a narrative review.(VIII) Generating political support for cardiac surgical care in resource-limited contexts: experience from Nepal.(IX) Ethics of resource allocation to congenital heart surgery in variable-resource contexts.This special series may expand upon and accelerate the contemporary global health discourse, which largely lacks the integration of pediatric and congenital cardiovascular care.Without an urgent recognition of the importance of pediatric and congenital cardiovascular care, the 2030 United Nations Sustainable Development Goal Agenda cannot and will not be attained (9). Global disparitiesRecent data confirm great disparities in the number of pediatric cardiac surgeons per million population.In high-income countries, there are approximately 9.51 pediatric cardiac surgeons per million under-15 population compared to only 0.07 per million in low-income countries (10).However, the number of pediatric cardiac surgeons managing neonates and infants with CHD is assumed to be far lower, although not exactly quantified (11).As such, Murala et al. (12).discuss opportunities to scale pediatric and congenital cardiac care capacity through the lens of the ongoing COVID-19 pandemic, which exacerbated disparities in access to cardiac care (13,14).These opportunities include but are not limited to (I) frugal innovation, which has enabled programs to do more with less as a result of resource constraints and lacking supply chains (15,16); (II) online learning, which reduced barriers to educational participation by bringing workshops and classrooms into one's own home, regardless of one's location (17,18); and (III) simulation training, which facilitates technical skills training in a low-, medium-, or even high-fidelity manner when real-world opportunities are not available or the risks of real-world exposure are too high (19)(20)(21).Disparities further vary by and within regions (22).For example, Manuel et al. (23) illustrate how, in sub-Saharan Africa, late diagnosis after the first year of life is common and associated with considerably higher mortality, reduced access to

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.379
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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