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Record W2159561148 · doi:10.1186/s40360-015-0006-0

A Swiss real world best practice experience in three different clinical settings of the 6 hour fingolimod first dose observation procedure

2015· article· en· W2159561148 on OpenAlexaboutno aff
Simon Ramseier, Serge Roth, Adam Czapliński

Bibliographic record

VenueBMC Pharmacology and Toxicology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSphingolipid Metabolism and Signaling
Canadian institutionsnot available
FundersNovartis Pharma
KeywordsFingolimodMedicineTolerabilityMultiple sclerosisObservational studyClinical PracticePediatricsAdverse effectInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: The Swiss label of oral fingolimod (0.5 mg once daily) requires a 6-hour first dose observation (FDO) including an ECG prior to and 6 hours after the first intake but in comparison to other countries such as Austria, Australia and Canada there are no restrictions regarding the clinical settings of the FDO procedure in Switzerland. We present here our real-world experience of the 6 hour FDO procedure in three different clinical settings, following fingolimod treatment initiation. This is the first report on the FDO of fingolimod in these real-world clinical settings in Swiss patients with multiple sclerosis (MS). METHODS: This was a retrospective, multi-clinic, observational study of 136 patients with relapsing-remitting multiple sclerosis. Summary statistics have been used to present the data. RESULTS: Only two patients (<1.5% [2/136]) experienced symptoms after the first dose of fingolimod. Atrioventricular conduction abnormalities were reported in 3% (4/136) of patients, which resolved spontaneously within 24 hours of treatment initiation. During the average 6.8 months follow-up, 96% (131/136) of the patients remained on therapy CONCLUSIONS: These findings support the safety and feasibility of FDO and tolerability of fingolimod in real-world clinical settings.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.081
GPT teacher head0.382
Teacher spread0.301 · 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.

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

Citations5
Published2015
Admission routes1
Has abstractyes

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