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Record W2984784734 · doi:10.22215/etd/2018-13492

The Impact of Mesenchymal Stem Cell Therapy on Cognitive Performance in People Living with Multiple Sclerosis

2018· dissertation· en· W2984784734 on OpenAlexaff
Maha Abu-AlHawa

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsCognitionMultiple sclerosisDepression (economics)DiseaseAnxietyMedicineMesenchymal stem cellPhysical medicine and rehabilitationNeurosciencePsychologyPhysical therapyPathologyPsychiatry

Abstract

fetched live from OpenAlex

In Multiple Sclerosis (MS), cognition is directly affected by neural integrity and secondary disease characteristics (anxiety, depression and fatigue). Mesenchymal stem cell therapy (MSCT) has been recently studied due to its potential for neural repair in MS. It is hypothesized that cognitive improvement will be seen after MSCT. The change in cognition is expected to be correlated with changes in secondary disease characteristics and neurophysiological measures (neural conduction time). Ten participants received MSCT. Cognition, secondary disease characteristics, and conduction time were evaluated pre- and post- (12 month) therapy. Eight participants demonstrated cognitive improvement in at least one test. Secondary disease characteristics and conduction time were not associated with cognition in our sample. In conclusion, MSCT appears to be safe with regards to cognition and does not worsen secondary disease characteristics. Future efficacy studies have the potential to show greater cognitive improvement than the current preliminary study.

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)0.002
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.052
GPT teacher head0.324
Teacher spread0.272 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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