MétaCan
Menu
Back to cohort

Valuing gene testing in children with possible neurofibromatosis 1

2011· article· en· W2066084358 on OpenAlexafffund
Erica S. Tsang, Patricia Birch, Jan M. Friedman

Bibliographic record

VenueClinical Genetics · 2011
Typearticle
Languageen
FieldMedicine
TopicNeurofibromatosis and Schwannoma Cases
Canadian institutionsChild and Family Research InstituteUniversity of British Columbia
FundersFaculty of Medicine, University of British ColumbiaChild and Family Research Institute
KeywordsNeurofibromatosisGenetic testingMedical diagnosisMedicineGenetic diagnosisHealth careDiagnostic testGenetic counselingMEDLINEPediatricsIntensive care medicineFamily medicinePathologyGenetics

Abstract

fetched live from OpenAlex

With the growing number of clinical guidelines recommending genetics tests in routine clinical care, the value of these tests should be evaluated. We examined the economic value of offering genetic testing to children with possible neurofibromatosis 1 (NF1) in British Columbia. Diagnosis of NF1 is usually made based on diagnostic clinical criteria, but molecular diagnostic testing, currently offered on a case-by-case basis in BC, now reliably diagnoses NF1 in 95% of cases. Children who present with some clinical features but whose findings are insufficient to meet the diagnostic criteria are labelled as having 'possible NF1'. Current guidelines call for these children to be followed as they have NF1, leading to annual ophthalmologic examinations and screening for complications; thus, there are increased costs to health care system. We created a model to account for these costs to the health care system, comparing the current protocol with one that would offer all children diagnosed with possible NF1 with genetic testing. Focusing on the incremental cost allowed us to determine that genetic testing provides good value, and patient interviews provided insight into the qualitative benefits of an earlier firm diagnosis. These findings may be helpful in guiding health policy decision-making.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.644

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 head0.129
GPT teacher head0.326
Teacher spread0.197 · 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

Citations8
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueClinical GeneticsSame topicNeurofibromatosis and Schwannoma CasesFrench-language works237,207