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Record W4386620056 · doi:10.1101/2023.09.08.23295195

Clinical actionability of genetic findings in cerebral palsy

2023· preprint· en· W4386620056 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

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of TorontoHolland Bloorview Kids Rehabilitation HospitalSickKids FoundationHospital for Sick Children
FundersNational Institute of Neurological Disorders and StrokeNational Institutes of HealthDeutsche ForschungsgemeinschaftUS-UK Fulbright CommissionFulbright AustraliaAustralian GovernmentTechnische Universität MünchenBundesministerium für Bildung und ForschungCerebral Palsy Alliance Research Foundation
KeywordsMedicinePsychological interventionIntervention (counseling)EtiologyGenetic counselingExome sequencingCerebral palsyGenetic testingDelphi methodBioinformaticsPhysical therapyInternal medicineGeneticsPhenotypePsychiatryGeneBiology

Abstract

fetched live from OpenAlex

ABSTRACT Background and objectives Single gene mutations are increasingly recognized as causes of cerebral palsy (CP) phenotypes, yet there is currently no standardized framework for measuring their clinical impact. We evaluated Pathogenic/Likely Pathogenic (P/LP) variants identified in individuals with CP to determine how frequently genetic testing results would prompt changes in care. Methods We analyzed published P/LP variants in OMIM genes identified in clinical (n = 1,345 individuals) or research (n = 496) cohorts using exome sequencing of CP patients. We established a working group of clinical and research geneticists, developmental pediatricians, genetic counselors, and neurologists and performed a systematic review of existing literature for evidence of clinical management approaches linked to genetic disorders. Scoring rubrics were adapted, and a modified Delphi approach was used to build consensus and establish the anticipated impact on patient care. Overall clinical utility was calculated from metrics assessing outcome severity if left untreated, safety/practicality of the intervention, and anticipated intervention efficacy . Results We found 140/1,841 (8%) of individuals in published CP cohorts had a genetic diagnosis classified as actionable , defined as prompting a change in clinical management based on knowledge related to the genetic etiology. 58/243 genes with P/LP variants were classified as actionable; 16 had treatment options targeting the primary disease mechanism , 16 had specific prevention strategies , and 26 had specific symptom management recommendations. The level of evidence was also graded according to ClinGen criteria; 44.6% of interventions had evidence class “D” or below. The potential interventions have clinical utility with 97% of outcomes being moderate-high severity if left untreated and 62% of interventions predicted to be of moderate-high efficacy . Most interventions (71%) were considered moderate-high safety/practicality . Discussion Our findings indicate that actionable genetic findings occur in 8% of individuals referred for genetic testing with CP. Evaluation of potential efficacy , outcome severity , and intervention safety / practicality indicates moderate-high clinical utility of these genetic findings. Thus, genetic sequencing to identify these individuals for precision medicine interventions could improve outcomes and provide clinical benefit to individuals with CP. The relatively limited evidence base for most interventions 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)0.080
metaresearch head score (Gemma)0.187
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.424

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.187
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0110.005
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.003
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 head0.069
GPT teacher head0.362
Teacher spread0.292 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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