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Pregabalin in the Treatment of Refractory Neuropathic Pain: Results of a 15-Month Open-Label Trial

2008· article· en· W1965202316 on OpenAlexaboutno aff
Brett R. Stacey, Robert H. Dworkin, Kevin Murphy, Uma Sharma, Birol Emir, T. Griesing

Bibliographic record

VenuePain Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
FundersPfizer
KeywordsPregabalinMedicineNeuropathic painOpen labelRefractory (planetary science)GabapentinAnesthesiaClinical trialAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Neuropathic pain associated with postherpetic neuralgia (PHN) and painful diabetic peripheral neuropathy (DPN) can be intractable and may not respond to commonly used treatments, such as tricyclic antidepressants (TCAs) and opioids. This long-term, open-label study was a preliminary evaluation of pregabalin for patients whose pain had been judged refractory to other treatments for neuropathic pain. DESIGN: Patients had previously participated in double-blind, placebo-controlled, randomized trials of pregabalin in DPN and PHN. They had moderate to severe neuropathic pain despite treatment with gabapentin, a TCA, and a third medication (e.g., other anticonvulsants, opioid, selective serotonin reuptake inhibitor, tramadol). Flexible-dosage pregabalin 150-600 mg/day was taken for 3-month periods followed by 3- to 28-day pregabalin "drug holidays," with an analysis up to 15 months (five treatment cycles). Pain intensity was measured using the visual analog scale of the Short-Form McGill Pain Questionnaire. RESULTS: In total, 81 patients were included in this analysis. Pregabalin 150-600 mg/day was associated with clinically meaningful and sustained pain reduction during each treatment cycle. During pregabalin "drug holidays," pain quickly returned to baseline levels; it was reduced again when pregabalin was reinstated. CONCLUSIONS: These results suggest that pregabalin may be beneficial in patients with neuropathic pain who have had an unsatisfactory response to treatment with other medications.

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)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.095
GPT teacher head0.339
Teacher spread0.244 · 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 designRandomized 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

Citations67
Published2008
Admission routes1
Has abstractyes

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