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Expected Benefits and Challenges of Using Economic Evaluations to Make Decisions About the Content of Newborn Screening Programs in Vietnam: A Scoping Review of the Literature

2023· review· en· W4375933178 on OpenAlexaff
Van Hoa Ho, Yves Giguère, Daniel Reinharz

Bibliographic record

VenueJournal of Inborn Errors of Metabolism and Screening · 2023
Typereview
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDeveloping countryCornerstonePillarNewborn screeningDeveloped countryPopulationMedicineEconomic growthDiseasePublic healthPublic economicsRisk analysis (engineering)Environmental healthEconomicsPediatricsEngineeringPathologyGeography

Abstract

fetched live from OpenAlex

Screening newborns for genetic and other diseases is one of the most effective ways to improve health and reduce disease in a population. In developed countries, newborn screening has been a cornerstone of public health for decades. In many developing countries, however, newborn screening is still in its infancy. Many countries still lack screening programs. When a program is available, it generally lacks well-defined criteria on which decision-makers can justify the choice of diseases screened for and the methods used. One of the reasons put forward to understand this observation is the fact that little consideration is given by decision-makers to economic evaluations as a pillar of decision-making, as is the case in industrialized countries. This article provides a brief description of the challenges of using economic evaluation of newborn screening in developing countries. This will be illustrated by the example of the national newborn screening program in Vietnam.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.356
GPT teacher head0.425
Teacher spread0.069 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes1
Has abstractyes

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