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Record W203680010

[Long-term efficacy and safety of pregabalin in patients with postherpetic neuralgia: results of a 52-week, open-label, flexible-dose study].

2010· article· en· W203680010 on OpenAlexaboutno aff
Setsuro Ogawa, Makoto Suzuki, Akio Arakawa, Tamotsu Yoshiyama, Misaki Suzuki

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPregabalinPostherpetic neuralgiaMedicineSomnolenceAdverse effectVisual analogue scaleAnesthesiaMcGill Pain QuestionnairePlaceboPeripheral edemaNeuropathic painInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The efficacy of pregabalin was demonstrated in a randomized double-blind placebo-controlled 13-week trial in 371 Japanese patients with postherpetic neuralgia (PHN). In this study, we evaluated the long-term efficacy and safety of pregabalin for relief of PHN. METHODS: 126 patients were enrolled from the preceding double-blind study into the 52-week open-label study. Patients were given pregabalin 150 to 600 mg x day(-1). Pain intensity was measured using the Short-Form McGill Pain Questionnaire (SF-MPQ: total score, visual analogue scale and present pain intensity). RESULTS: The efficacy parameter SF-MPQ showed a decrease over the treatment-term. The changes of visual analogue scale and present pain intensity at the endpoint were -28.3 mm and -1.1 score, respectively. The commonly reported adverse events were dizziness, somnolence, peripheral edema and weight gain, and most of them were mild to moderate in intensity. No new adverse events were observed due to long-term pregabalin administration. CONCLUSIONS: These results suggest that long-term treatment of pregabalin may be beneficial in patients with PHN.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.275
Teacher spread0.247 · 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 designNon-randomized trial
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

Citations17
Published2010
Admission routes1
Has abstractyes

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