MétaCan
Menu
Back to cohort
Record W2107017366 · doi:10.1177/0091270009333017

Trigeminal Neuralgia Treated With Pregabalin in Family Medicine Settings: Its Effect on Pain Alleviation and Cost Reduction

2009· article· en· W2107017366 on OpenAlexaboutno aff
Concepción Pérez, María T. Saldaña, Ana Navarro, Silvia González-Martínez, Javier Rejas

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2009
Typearticle
Languageen
FieldMedicine
TopicTrigeminal Neuralgia and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePregabalinTrigeminal neuralgiaObservational studyNeuropathic painHealth careNeuralgiaPhysical therapyEmergency medicineAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

The purpose of this study is to analyze the effect of pregabalin (PGB) on pain alleviation, use of health care and non-health care resources, and associated costs in patients with trigeminal neuralgia under usual clinical practice in primary care settings. Sixty-five PGB-naïve patients receiving PGB as monotherapy (n = 36, 55%) or combined with other drugs (n = 29, 45%) fulfill criteria for inclusion in a secondary analysis from a 12-week, multicenter, observational prospective study aimed to ascertain the cost of illness in subjects with neuropathic pain. Pain is evaluated using the Short Form McGill Pain Questionnaire. Use of health care and non-health care resources and lost workdays equivalents (LWDEs) are also recorded. PGB significantly reduces pain scores, use of health care resources (ancillary tests and unscheduled medical visits), and number of LWDEs. Additional cost of PGB treatment (+euro 174 +/- 106) is broadly compensated for by a reduction in both health care costs (-euro 621 +/-1211, P < .001) and indirect costs (-euro 1210 +/- 1141, P < .001). It is concluded that PGB as monotherapy or combined with other drugs is effective in pain management in patients with trigeminal neuralgia and reduces the cost of illness.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 integrity0.0000.001
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 head0.059
GPT teacher head0.433
Teacher spread0.374 · 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

Citations32
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Clinical PharmacologySame topicTrigeminal Neuralgia and TreatmentsFrench-language works237,207