MétaCan
Menu
Back to cohort
Record W1979442527 · doi:10.1213/ane.0b013e3181cf949a

Perioperative Pregabalin Improves Pain and Functional Outcomes 3 Months After Lumbar Discectomy

2010· article· en· W1979442527 on OpenAlexaboutno aff
Siun M. Burke, George Shorten

Bibliographic record

VenueAnesthesia & Analgesia · 2010
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePregabalinPerioperativePlaceboAnesthesiaVisual analogue scaleRadicular painLumbarDiscectomySurgeryLow back pain

Abstract

fetched live from OpenAlex

BACKGROUND: Patient outcome after lumbar discectomy for radicular low back pain is variable and the benefit is inconsistent. Many patients continue to experience pain 3 months after surgery. Pregabalin, a membrane stabilizer, may decrease perioperative central sensitization and subsequent persistent pain. METHODS: Forty patients undergoing lumbar discectomy were randomly allocated to receive either pregabalin (300 mg at 90 minutes preoperatively and 150 mg at 12 and 24 hours postoperatively) or placebo at corresponding times in a double-blinded manner. Our primary outcome was the change in the present pain intensity (PPI) (visual analog scale [VAS], 0-100 mm [PPI-VAS, McGill Pain Questionnaire]) from preoperatively to 3 months postoperatively. RESULTS: The decrease in PPI-VAS score at 3 months was greater in patients who received pregabalin (37.6 +/- 19.6 mm) (mean +/- sd) than those who received placebo (25.3 +/- 21.9 mm) (P = 0.08). The Roland Morris disability score at 3 months was less in patients who received pregabalin (2.7 +/- 2.4) than in those who received placebo (5.6 +/- 4.8) (P = 0.032). Pregabalin administration was associated with greater pain tolerance thresholds in both lower limbs compared with placebo at 24 hours postoperatively. CONCLUSION: Perioperative pregabalin administration is associated with less pain intensity and improved functional outcomes 3 months after lumbar discectomy.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.081
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.008
GPT teacher head0.246
Teacher spread0.238 · 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

Citations130
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueAnesthesia & AnalgesiaSame topicSpine and Intervertebral Disc PathologyFrench-language works237,207