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Clinical Actionability of Genetic Findings in Cerebral Palsy

2024· review· en· W4404905120 on OpenAlexaff
Sara A. Lewis, Maya Chopra, Julie S. Cohen, Jennifer Bain, Bhooma R. Aravamuthan, Jason B. Carmel, Michael Fahey, Reeval Segel, Richard F. Wintle, Michael Zech, Halie May, N Haque, Darcy Fehlings, Siddharth Srivastava, Michael C. Kruer

Bibliographic record

VenueJAMA Pediatrics · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversity of TorontoHolland Bloorview Kids Rehabilitation HospitalSickKids FoundationHospital for Sick Children
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Neurological Disorders and Stroke
KeywordsMedicineEtiologyExome sequencingPsychological interventionGenetic testingBioinformaticsGeneticsPhenotypeGeneInternal medicinePsychiatryBiology

Abstract

fetched live from OpenAlex

Importance: Single gene variants can cause cerebral palsy (CP) phenotypes, yet the impact of genetic diagnosis on CP clinical management has not been systematically evaluated. Objective: To evaluate how frequently genetic testing results would prompt changes in care for individuals with CP and the clinical utility of precision medicine therapies. Data Sources: Published pathogenic or likely pathogenic variants in OMIM genes identified with exome sequencing in clinical (n = 1345) or research (n = 496) cohorts of CP were analyzed. A systematic literature review for evidence of effective therapies for specific genetic etiologies was performed. Study Selection: Nonstandard interventions that led to a detectable improvement in a defined outcome in individuals with variants in the gene of interest were included. Data Extraction and Synthesis: Literature was evaluated using PRISMA guidelines. A diverse, expert working group was established, scoring rubrics adapted, and scoring consensus built with a modified Delphi approach. Main Outcomes and Measures: Overall clinical utility was calculated from metrics assessing outcome severity if left untreated, safety and practicality of the intervention, and anticipated intervention efficacy on a scale from 0 to 3. Results: Of 1841 patients with CP who underwent exome sequencing, 502 (27%) had pathogenic or likely pathogenic variants related to their phenotype. A total of 243 different genes were identified. In 1841 patients with identified genetic etiologies of CP, 140 (8%) had a genetic etiology classified as actionable, defined as prompting a change in clinical management. Also identified were 58 of 243 genes with pathogenic or likely pathogenic variants with actionable treatment options: 16 targeting the primary disease mechanism, 16 with specific prevention strategies, and 26 with specific symptom management. The level of evidence was also graded according to ClinGen criteria; 45 of 101 interventions (44.6%) had evidence class D or below. The potential interventions have clinical utility with 98 of 101 outcomes (97%) being moderate-high severity if left untreated and 63 of 101 interventions (62%) predicted to be of moderate-high efficacy. Most interventions (72 of 101 [71%]) were considered moderate-high safety and practicality. Conclusions and Relevance: The findings indicate that actionable genetic findings occurred in 8% of individuals referred for genetic testing with CP. Evaluation of potential efficacy, outcome severity, and intervention safety and practicality indicates moderate-high clinical utility of these genetic findings. Genetic sequencing can identify precision medicine interventions that provide clinical benefit to individuals with CP. The relatively limited evidence base underscores the need for additional research.

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)0.156
metaresearch head score (Gemma)0.323
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.156
Threshold uncertainty score0.826

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1560.323
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0150.010
Science and technology studies0.0010.004
Scholarly communication0.0030.004
Open science0.0020.004
Research integrity0.0020.001
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 head0.031
GPT teacher head0.356
Teacher spread0.324 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2024
Admission routes1
Has abstractyes

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