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Record W2290554811 · doi:10.14740/jocmr2495w

Efficacy of Stem Cell Therapy in Amyotrophic Lateral Sclerosis: A Systematic Review and Meta-Analysis

2016· review· en· W2290554811 on OpenAlexvenueno aff
Mirian Conceição Moura, María Rita Carvalho Garbi Novaes, Yuri S. S. P. Zago, Emanoel Junio Eduardo, Luíz Augusto Casulari

Bibliographic record

VenueJournal of Clinical Medicine Research · 2016
Typereview
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisAmyotrophic lateral sclerosisStem cellStem-cell therapyAdverse effectRetrospective cohort studyConfidence intervalClinical trialInternal medicineOncologyDiseaseTransplantation

Abstract

fetched live from OpenAlex

BACKGROUND: Published studies seeking to improve survival in amyotrophic lateral sclerosis (ALS) have poor results in humans, although there are several studies in animal models with positive results. METHODS: We conducted a systematic review and meta-analysis of studies that were published between March 2009 and March 2015 on stem cell therapy and survival in animal models and patients with ALS. A total of 714 articles were identified, and from these, we selected preclinical in vivo studies and retrospective clinical studies. RESULTS AND CONCLUSIONS: A meta-analysis confirmed the efficacy of stem cell therapy in improving survival in preclinical trials, where a mean difference of 9.79 days (95% confidence interval: 4.45 - 15.14) in lifespan favored stem cell therapy. In contrast, the number of clinical studies is still insufficient to assess their effectiveness, and these studies only demonstrate the absence of serious adverse events. However, even this conclusion should be interpreted with caution because clinical studies are retrospective and heterogeneous and have an unsatisfactory quality.

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)0.047
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0330.007
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
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 head0.613
GPT teacher head0.578
Teacher spread0.035 · 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 designSystematic review
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

Citations20
Published2016
Admission routes1
Has abstractyes

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