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Record W2142305564 · doi:10.1017/s0317167100008611

Pilot Study of Minocycline in Relapsing-Remitting Multiple Sclerosis

2008· article· en· W2142305564 on OpenAlexaffvenue
Yunyan Zhang, Luanne M. Metz, V. Wee Yong, Robert B. Bell, Michael Yeung, David Patry, J. Ross Mitchell

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2008
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMinocyclineMultiple sclerosisMedicineTolerabilityRelapsing remittingInternal medicineSurgeryAdverse effectAntibioticsImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Current multiple sclerosis (MS) treatment is only partially effective and not all patients respond well. The goal in this study was to evaluate minocycline for its safety, tolerability, and MRI impact as a potential therapy over 36 months after a three month run-in in ten relapsing-remitting (RR) MS patients. METHODS: Clinical assessments were at three month intervals until six months, then at six month intervals. Three Tesla MRI was performed monthly during the run-in and first six months of treatment, then at 12, 24, and 36 months. RESULTS: Treatment was safe and well tolerated. Annualized relapse rate was 1.2 during the run-in and 0.25 during treatment. The proportion of active scans was lower during the first six months of treatment (5.6%, p < 0.001) and during the extension (8.7%, p = 0.002) than during the run-in (47.5%). Consistent with these outcomes, mean T2 lesion volume remained stable over three years and percent brain volume change was reduced during year three (-0.37%) of minocycline treatment. CONCLUSIONS: This trial is limited by small sample and no control group but suggests that minocycline is safe and potentially beneficial in RRMS. This supports further investigation of its efficacy.

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)0.005
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.008
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.193
GPT teacher head0.325
Teacher spread0.132 · 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; both teacher heads agree on what is shown here.

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

Citations70
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences NeurologiquesSame topicMultiple Sclerosis Research StudiesFrench-language works237,207