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Record W4324117460 · doi:10.1016/j.gimo.2023.100421

P385: Understanding clinician needs and preferences with respect to returned NBS results

2023· article· en· W4324117460 on OpenAlexaboutno aff
Karen Eilbeck, Nicole Ruiz-Schultz, Evan Christiansen, Andreas Rohrwasser

Bibliographic record

VenueGenetics in Medicine Open · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMedicine

Abstract

fetched live from OpenAlex

text review, and 25 were included.Data on patient characteristics, GS methods, turn-around time, diagnostic rate, and change in clinical management were extracted.How clinical utility was defined and measured across publications was synthesized thematically.Guided by existing C-GUIDE items and this scoping review, a preliminary 21-item modified C-GUIDE was created.Clinicians were recruited to participate in qualitative interviews about the relevance, comprehensibility, and comprehensiveness of the modified C-GUIDE to gauge content validity and refine the preliminary list.Eligible clinicians included geneticists, neonatologists, and neurologists routinely involved in genetic testing and/or managing results in NICU settings across Canada.Interviews were conducted virtually, audio-taped, and transcribed.Item-by-item and thematic analyses enabled item refinement and further characterization of this construct of clinical utility.Results: Across 25 publications, clinical utility in this setting was most often defined as a change in clinical management following genetic testing (based on retrospective chart review), with only 7 studies utilizing clinician surveys to assess perceived utility.Across the studies, diagnostic rates of 12-60% were reported for GS and changes in management were reported in 22-100%.Seven putative domains of clinical utility for GS in NICU settings were identified, pertaining to: 1) diagnosis, differential diagnosis and work-up; 2) clinical management; 3) prognosis; 4) referrals; 5) disposition planning; 6) family planning; and 7) psychosocial impact.The role of genotype in selecting pharmaceutical, dietary, and targeted therapeutic interventions, and its contribution to discussions on redirection of care were particularly salient.To date, 14 qualitative interviews with 9 medical geneticists, 4 neonatologists, and 1 neurologist based in 4 Canadian provinces have been completed.Qualitative analysis revealed the following themes: 1) perceived clinical utility is improved across all domains with more rapid turn-around times; 2) GS can allow for cessation of a potentially invasive work-up; 3) streamlined management plans guided by prognosis can improve quality of life for neonates; 4) provision of an "answer" can improve parental understanding, strengthen decision-making, and reduce guilt; and 5) genetic information can facilitate essential social connection via identification of other families and support groups.Themes of "disutility" included: 1) potential prolongation of ICU stay while awaiting GS results; and 2) familial psychosocial harm, in general, associated with the pursuit of genetic testing.Conclusion: While the literature highlighted a high frequency of diagnosis and changes in medical management associated with GS in NICU settings, a broader definition of clinical utility is lacking with no standardized methods to capture clinical utility currently in use.Our preliminary, expert-informed, modified C-GUIDE defines clinical utility as a broad construct that contributes to diagnosis and prognosis, reduction in diagnostic work-up, clinical management, informed discussions on goals of care, disposition planning, and family psychosocial functioning.Qualitative interviews will be completed by the end of 2022, and itembased feedback will be synthesized to guide the development of a refined item list through a Delphi consensus process.Following validation, C-GUIDE NICU will be available for use.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.370
GPT teacher head0.396
Teacher spread0.026 · 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 designTheoretical or conceptual
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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