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Record W4225834053 · doi:10.3390/ijns8010005

International Approaches to Management of CFTR-Related Metabolic Syndrome/Cystic Fibrosis Screen Positive, Inconclusive Diagnosis

2022· article· en· W4225834053 on OpenAlexaboutno aff
Jane Chudleigh, Jürg Barben, Clement L. Ren, Kevin W Southern

Bibliographic record

VenueInternational Journal of Neonatal Screening · 2022
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsnot available
Fundersnot available
KeywordsCystic fibrosisCystic fibrosis transmembrane conductance regulatorAnxietyMedicineComputer-assisted web interviewingHealth professionalsClinical PracticeFamily medicinePsychiatryClinical psychologyHealth careInternal medicine

Abstract

fetched live from OpenAlex

The main aim of the present study was to explore health professionals' reported experiences and approaches to managing children who receive a designation of cystic fibrosis transmembrane conductance regulator-related metabolic syndrome/cystic fibrosis screen positive inconclusive diagnosis following a positive NBS result for cystic fibrosis. An online questionnaire was distributed via Qualtrics Survey Software and circulated to a purposive, international sample of health professionals involved in managing children with this designation. In total, 101 clinicians completed the online survey: 39 from the US, six from Canada, and 56 from Europe (including the UK). Results indicated that while respondents reported minor deviations in practice, they were cognizant of recommendations in the updated guidance and for the most part, attempted to implement these into practice consistently internationally. Where variation was reported, the purpose of this appeared to be to enable clinicians to respond to either clinical assessments or parental anxiety in order to improve outcomes for the child and family. Further research is needed to determine if these findings are reflective of both a wider audience of clinicians and actual (rather than reported) practice.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.303
Teacher spread0.268 · 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.

Study designOther design
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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Neonatal ScreeningSame topicCystic Fibrosis Research AdvancesFrench-language works237,207