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Record W7132916377

Health Care Providers' Perspectives of Uncertainty in Newborn Screening

2018· dissertation· W7132916377 on OpenAlexaffabout
Paul John Azzopardi

Bibliographic record

VenueTSpace · 2018
Typedissertation
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsInstitute of Health Services and Policy Research
Fundersnot available
KeywordsNewborn screeningContext (archaeology)Health careQualitative researchHealth screeningMEDLINE
DOInot available

Abstract

fetched live from OpenAlex

There is a paucity of research exploring the issues of uncertainty in the context of newborn screening and metabolic care. This work explores these issues of uncertainty through qualitative description. Semi-structured telephone interviews were conducted with health care providers at specialized metabolic centers across Canada. Data was coded and thematically analyzed. This study found that health care providers experience personal, practical, diagnostic, prognostic, and therapeutic issues of uncertainty when managing the care of patients affected by mild hyperphenylalaninemia (MHP), very long chain acyl CoA dehydrogenase (VLCAD) deficiency, medium chain acyl CoA dehydrogenase (MCAD) deficiency, and partial biotinidase deficiency. Heath care providers described nosological inadequacy as a source of uncertainty when managing 3-methylcrotonyl CoA (3-MCC) deficiency. Participants emphasized caution, while avoiding overmedicalization, when managing medical uncertainty. Providers indicated that greater communication and consensus is required across care centers, which may open a dialogue for a pan-Canadian newborn screening strategy.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.036
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.021
Scholarly communication0.0080.005
Open science0.0010.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.357
Teacher spread0.342 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2018
Admission routes2
Has abstractyes

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