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Record W4221000033 · doi:10.3390/ijns8020026

Need and Viability of Newborn Screening Programme in India: Report from a Pilot Study

2022· article· en· W4221000033 on OpenAlexfundaboutno aff
Arya Raveendran, Teena Joseph Chacko, Priya Prabhu, Raghava Varma, Leslie Lewis, Pragna Rao, Prajna Shetty, Yajna S. Phaneendra Mallimoggala, Asha Hedge, Dinesh Nayak, Sudeep Moorkoth, Sudheer Moorkoth

Bibliographic record

VenueInternational Journal of Neonatal Screening · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsnot available
FundersVision Group on Science and TechnologyGrand Challenges CanadaGovernment of Canada
KeywordsMedicineNewborn screeningEnvironmental healthPopulationIncidence (geometry)Family medicinePediatrics

Abstract

fetched live from OpenAlex

India, a country with the second largest population in the world, does not have a national newborn screening programme as part of its health policy. With funding support from the Grand Challenges Canada, a pilot newborn screening programme was implemented for the Udupi district of South India to study the need and viability of a national programme in India. Six disorders were selected for the study based on the availability of funding and recommendation from pediatricians in the district. Here, we report the observed incidence during the study. A cost-effectiveness analysis of implementing newborn screening in India was performed. It is evident from our analysis that the financial loss for the nation due to these preventable diseases is much higher than the overall expenditure for screening, diagnosis, and treatment. This cost-effectiveness analysis justifies the need for a national newborn screening programme in India.

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.

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 score0.298
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.275
Teacher spread0.257 · 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

Citations24
Published2022
Admission routes2
Has abstractyes

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