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Record W4210702305 · doi:10.1161/str.53.suppl_1.tp172

Abstract TP172: Clinical And Neuroimaging Predictors Of Dyskinetic Cerebral Palsy

2022· article· en· W4210702305 on OpenAlexaff
Liam Sanvido, Pradeep Krishnan, Trish Domi, Kirstin Walker, Darcy Fehlings, Amanda Robertson, Ritesh Thapa, Nomazulu Dlamini

Bibliographic record

VenueStroke · 2022
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsSickKids FoundationHolland Bloorview Kids Rehabilitation HospitalHospital for Sick ChildrenUniversity of Ottawa
Fundersnot available
KeywordsMedicineNeuroimagingCerebral palsyInternal capsuleLesionMagnetic resonance imagingPeriventricular leukomalaciaGestational ageThalamusGross Motor Function Classification SystemPediatricsRadiologyPhysical medicine and rehabilitationSurgeryPregnancyPsychiatry

Abstract

fetched live from OpenAlex

Background: Dyskinetic cerebral palsy (DCP) is a non-progressive disorder that results from lesions to the developing fetal brain. Neuroimaging patterns and risk factors for DCP in pre-term infants are poorly understood. Furthermore, neuroimaging differences between preterm and term infants with DCP and its relationship to clinical outcomes are not well established. Objectives: 1) To describe neuroimaging differences between term and preterm infants with DCP. 2) To investigate relationships between neuroimaging patterns and clinical motor outcome of infants with DCP. Methods: Patients with DCP were identified through The Cerebral Palsy Network where clinical details and magnetic resonance images (MRI) were collected on children with DCP. To determine lesion volume and location, manual segmentation was performed using ITK-SNAP software by study neuroradiologist. Lesion severity was graded using a semi-quantitative scale for structural MRIs(Laporta-Hoyos et al. 2018) based on the number of lobes and subcortical structures involved. Motor outcomes was assessed with the Gross Motor Function Classification System. Results: Twenty-nine patients with DCP were identified. Preterm infants (n=12, [male= 8, female=4]), and term infants (n=16 term, [male= 6, female=10]) differed in mean gestational age (30.5 versus 39.1 weeks respectively, p=.02). The frequency of periventricular leukomalacia in preterm (89%) and term-born (58%) infants differed (p = 0.02). Lesion severity was associated with: the ventral posterior lateral (VPL) thalamus (r=0.574,p =.01), the posterior limb of the internal capsule (PLIC) (r=0.437p = .04) in term infants, and the right (r = 0.504, p = .01) and left putamen r = 0.629, p = .002) in term infants. Significant negative correlations were found between lesion severity and gross motor function in the VPL thalamus (r= -0.624, p = .001), PLIC (r= -0.735, p < .001), right (r= -0.50, p= .016) and left putamen (r= -0.54 p = .008). Conclusion: Our results suggest the timing of lesions in term-born and preterm infants with DCP are associated with neuroimaging lesion patterns. Involvement of subcortical structures and lesion severity scores were associated with gross motor function in this cohort of patients with DCP.

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)0.000
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.022
GPT teacher head0.293
Teacher spread0.271 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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