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Record W2032249934 · doi:10.1111/dmcn.12166

The potential for stem cell therapies to have an impact on cerebral palsy: opportunities and limitations

2013· review· en· W2032249934 on OpenAlexaff
Crystal A. Ruff, Stuart Faulkner, Michael G. Fehlings

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsCerebral palsyContext (archaeology)NeuroscienceNeural stem cellTransplantationStem cellMedicineMesenchymal stem cellOlfactory ensheathing gliaRegenerative medicineStem-cell therapyBioinformaticsPsychologyCentral nervous systemBiologyPathologyPhysical medicine and rehabilitationSurgery

Abstract

fetched live from OpenAlex

Cerebral palsy (CP) is a chronic childhood disorder described by a group of motor and cognitive impairments and results in a substantial socio-economic burden to the individual, family, and healthcare system. With no effective biological interventions, therapies for CP are currently restricted to supportive and management strategies. Cellular transplantation has been suggested as a putative intervention for neural pathology, as mesenchymal and neural stem cells, as well as olfactory ensheathing glia and Schwann cells, have shown some regenerative and functional efficacy in experimental central nervous system disorders. This review describes the most common cell types investigated and delineates their purported mechanisms in vivo. Furthermore, it provides a cogent summary of both current early-phase clinical trials using neural precursor cells (NPCs) and the state of stem cell therapies for neurodegenerative conditions. Although NPCs are perhaps the most promising candidates for cell replacement therapy in the context of CP, much still remains to be understood regarding safety, efficacy, timing, dose, and route of transplantation, as well as the capacity for combinatorial strategies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.091
GPT teacher head0.334
Teacher spread0.244 · 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.

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

Citations39
Published2013
Admission routes1
Has abstractyes

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