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Record W2979645247 · doi:10.1038/s41436-019-0670-3

Health-care providers’ perspectives on uncertainty generated by variant forms of newborn screening targets

2019· article· en· W2979645247 on OpenAlexafffund
Paul John Azzopardi, Ross Upshur, Stephanie Luca, Viji Venkataramanan, Beth K. Potter, Pranesh Chakraborty, Robin Z. Hayeems

Bibliographic record

VenueGenetics in Medicine · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsUniversity of OttawaChildren's Hospital of Eastern OntarioInstitute for Clinical Evaluative SciencesUniversity of TorontoSickKids FoundationHospital for Sick ChildrenPublic Health Ontario
FundersHospital for Sick ChildrenUniversity of Toronto
KeywordsNewborn screeningHealth careMedicineFamily medicinePsychologyPediatrics

Abstract

fetched live from OpenAlex

PURPOSE: Despite the public health successes of newborn bloodspot screening, uncertainty associated with variant forms of primary screening targets has led to discrepancies in medical management. This study explored health-care providers' approaches to managing atypical forms of inherited metabolic diseases (IMDs) in the absence of evidence-based guidelines. METHODS: Semistructured telephone interviews were conducted with metabolic specialists. 3-Methylcrotonyl CoA deficiency and variant forms of phenylketonuria, biotinidase deficiency, and fatty acid oxidation disorders were considered. Data were analyzed inductively and deductively using a novel taxonomy of uncertainty. RESULTS: Health-care providers (n = 12) navigate diagnostic, prognostic, and therapeutic challenges of uncertainty while interpreting patient and family attitudes, preferences, and ideas in the care of children with these result types. Participants explained the limits of classifying mild and atypical metabolic phenotypes. Participants also described the challenge of finding balance between cautious care and overmedicalization. Developing consistent care plans and honest communication with families were perceived as effective strategies when navigating uncertainty. CONCLUSION: Providers' experiences suggest a need for transparent and accessible guidelines that account for challenges associated with uncertainty generated by screening. Timely consideration of this challenge is warranted with increasing emergence of genotype-first approaches to screening.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.271
Teacher spread0.263 · 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 designBench or experimental
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

Citations19
Published2019
Admission routes2
Has abstractyes

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