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Record W6976858217 · doi:10.60692/pbbvf-3bb05

Using an experiment among clinical experts to determine the cost and clinical impact of rapid whole exome sequencing in acute pediatric settings

2023· article· en· W6976858217 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldHealth Professions
TopicSocial and Demographic Issues in Germany
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)Cost effectivenessMEDLINEQuality managementExpert elicitationQuality (philosophy)Cost–benefit analysis

Abstract

fetched live from OpenAlex

Objective Evaluate the cost and clinical impacts of rapid whole-exome sequencing (rWES) for managing pediatric patients with unknown etiologies of critical illnesses through an expert elicitation experiment. Method Physicians in the intervention group ( n = 10) could order rWES to complete three real-world case studies, while physicians in the control group ( n = 8) could not. Costs and health outcomes between and within groups were compared. Results The cost incurred in the intervention group was consistently higher than the control by 60,000–70,000 THB. Fewer other investigation costs were incurred when rWES could provide a diagnosis. Less cost was incurred when an rWES that could lead to a change in management was ordered earlier. Diagnostic accuracy and the quality of non-pharmaceutical interventions were superior when rWES was available. Conclusion In acute pediatric settings, rWES offered clinical benefits at the average cost of 60,000–70,000 THB. Whether this test is cost-effective warrants further investigations. Several challenges, including cost and ethical concerns for assessing high-cost technology for rare diseases in resource-limited settings, were potentially overcome by our study design using expert elicitation methods.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.235
GPT teacher head0.464
Teacher spread0.230 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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