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Is there a role for combined use of Gabapentin and Pregabalin in pain control? Too good to be true?

2018· article· en· W2805605813 on OpenAlexaff
Helen Senderovich, Geetha Jeyapragasan, Michal Moshkovich, Shaira Wignarajah

Bibliographic record

VenueFaculty of 1000 Research Ltd · 2018
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsBaycrest Hospital
Fundersnot available
KeywordsGabapentinPregabalinOpen peer reviewPain controlPlant biologyMedicineAnesthesiologyAnesthesiaNeurosciencePharmacologyAlternative medicinePsychologyBiologyPathology

Abstract

fetched live from OpenAlex

Gabapentin (Neurontin) and Pregabalin (Lyrica) are first and second-generation α2delta ligands, respectively, and are both approved for use as adjunctive therapy in pain control. Their mechanism of action is not yet fully understood, but research has demonstrated promising results. Despite their similarities, they have been used in combination in both clinical and research settings, and have been noted to have a synergistic effect in pain control without concern for clinically significant pharmacokinetic interactions. This combined approach can be valuable in pain management by reducing the dose of individual agent, its side effects, and to enhance therapeutic response compared to a single agent in resistant cases.

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)0.017
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0050.010
Open science0.0040.002
Research integrity0.0090.014
Insufficient payload (model declined to judge)0.0110.004

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 head0.077
GPT teacher head0.368
Teacher spread0.291 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2018
Admission routes1
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