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Record W2077993189 · doi:10.1016/j.pain.2014.05.009

Effect of variability in the 7-day baseline pain diary on the assay sensitivity of neuropathic pain randomized clinical trials: An ACTTION study

2014· article· en· W2077993189 on OpenAlexaff
John T. Farrar, Andrea B. Troxel, Kevin Haynes, Ian Gilron, Robert D. Kerns, Nathaniel P. Katz, Bob A. Rappaport, Michael C. Rowbotham, Ann Tierney, Dennis C. Turk, Robert H. Dworkin

Bibliographic record

VenuePain · 2014
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsQueen's University
FundersU.S. Public Health Service
KeywordsNeuropathic painMedicineRandomized controlled trialClinical trialBaseline (sea)Physical therapyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

The degree of variability in the patient baseline 7-day diary of pain ratings has been hypothesized to have a potential effect on the assay sensitivity of randomized clinical trials of pain therapies. To address this issue, we obtained clinical trial data from the Food and Drug Administration (FDA) through the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership, and harmonized patient level data from 12 clinical trials (4 gabapentin and 8 pregabalin) in postherpetic neuralgia (PHN) and painful diabetic peripheral neuropathy (DPN). Models were developed using exploratory logistic regression to examine the interaction between available baseline factors and treatment (placebo vs active medication) in predicting patient response to therapy (ie, >30% improvement). Our analysis demonstrated an increased likelihood of response in the placebo-treated group for patients with a higher standard deviation in the baseline 7-day diary without affecting the likelihood of a response in the active medication-treated group, confirming our hypothesis. In addition, there was a small but significant age-by-treatment interaction in the PHN model, and small weight-by-treatment interaction in the DPN model. The patient's sex, baseline pain level, and the study protocol had an effect only on the likelihood of response overall. Our results suggest the possibility that, at least in some disease processes, excluding patients with a highly variable baseline 7-day diary has the potential to improve the assay sensitivity of these analgesic clinical trials, although reductions of external validity must be considered when increasing the homogeneity of the investigated sample.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Simulation or modelingmedium
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.816
metaresearch head score (Gemma)0.845
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.184
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8160.845
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0130.036
Bibliometrics0.0050.007
Science and technology studies0.0020.015
Scholarly communication0.0090.013
Open science0.0090.010
Research integrity0.0120.014
Insufficient payload (model declined to judge)0.0070.001

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 head0.066
GPT teacher head0.372
Teacher spread0.306 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designSimulation or modeling · Other design
DomainMethods
GenreEmpirical · Methods

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

Citations109
Published2014
Admission routes1
Has abstractyes

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