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Record W6958674641 · doi:10.6084/m9.figshare.c.4500434

MEsenchymal StEm cells for Multiple Sclerosis (MESEMS): a randomized, double blind, cross-over phase I/II clinical trial with autologous mesenchymal stem cells for the therapy of multiple sclerosis

2019· other· en· W6958674641 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2019
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsMesenchymal stem cellMultiple sclerosisClinical trialDiseaseStem cellPhases of clinical researchPlacebo

Abstract

fetched live from OpenAlex

Abstract Background Multiple sclerosis (MS) is an inflammatory disease of the central nervous system with a degenerative component, leading to irreversible disability. Mesenchymal stem cells (MSC) have been shown to prevent inflammation and neurodegeneration in animal models of MS, but no large phase II clinical trials have yet assessed the exploratory efficacy of MSC for MS. Methods/design This is an academic, investigator-initiated, randomized, double-blind, placebo-compared phase I/II clinical trial with autologous, bone-marrow derived MSC in MS. Enrolled subjects will receive autologous MSC at either baseline or at week 24, through a cross-over design. Primary co-objectives are to test safety and efficacy of MSC treatment compared to placebo at 6 months. Secondary objectives will evaluate the efficacy of MSC at clinical and MRI levels. In order to overcome funding constraints, the MEsenchymal StEm cells for Multiple Sclerosis (MESEMS) study has been designed to merge partially independent clinical trials, following harmonized protocols and sharing some key centralized procedures, including data collection and analyses. Discussion Results will provide patients and the scientific community with data on the safety and efficacy of MSC for MS. The innovative approach utilized to obtain funds to support the MESEMS trial could represent a new model to circumvent limitation of funds encountered by academic trials. Trial registration Andalusia: NCT01745783 , registered on Dec 10, 2012. Badalona: NCT02035514 EudraCT, 2010–024081–21. Registered on 2012. Canada: ClinicalTrials.gov, NCT02239393 . Registered on September 12, 2014. Copenhagen: EudraCT, 2012–000518-13 . Registered on June 21, 2012. Italy: EudraCT, 2011–001295-19, and ClinicalTrials.gov, NCT01854957 . Retrospectively registered on May 16, 2013. London: Eudra CT 2012–002357-35, and ClinicalTrials.gov, NCT01606215 . Registered on May 25, 2012. Salzburg: EudraCT, 2015–000137-78 . Registered on September 15, 2015. Stockholm: ClinicalTrials.gov, NCT01730547 . Registered on November 21, 2012. Toulouse: ClinicalTrials.gov, NCT02403947 . Registered on March 31, 2015.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.629
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.228
GPT teacher head0.327
Teacher spread0.099 · 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
GenreDataset

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
Published2019
Admission routes1
Has abstractyes

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