Effect of variability in the 7-day baseline pain diary on the assay sensitivity of neuropathic pain randomized clinical trials: An ACTTION study
Bibliographic record
Abstract
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.
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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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | medium |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.816 | 0.845 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.036 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".