What Makes Patients with Fibromyalgia Feel Better? Correlations Between Patient Global Impression of Improvement and Changes in Clinical Symptoms and Function: A Pooled Analysis of 4 Randomized Placebo-controlled Trials of Duloxetine
Bibliographic record
Abstract
Objective. To investigate the relationship between changes in clinical rating scale items and endpoint Patient Global Impression of Improvement (PGI-I). Methods. Data were pooled from 4 randomized, double-blind, placebo-controlled studies of duloxetine in patients with fibromyalgia (FM). Variables included in the analyses were those that assessed symptoms in FM domains of pain, fatigue, sleep, cognitive difficulties, emotional well-being, physical function, and impact on daily living. The association of endpoint PGI-I with changes from baseline in individual variables was assessed using Pearson product-moment correlations (r). Stepwise linear regression was used to identify those variables for which changes from baseline were statistically significant independent predictors of the endpoint PGI-I ratings. Results. Changes in pain variables and interference of symptoms with the ability to work were highly correlated (r ≥ 0.5 or r ≤ −0.5) with endpoint PGI-I. Moderate correlation with endpoint PGI-I (0.30 ≤ r < 0.5 or −0.5 < r ≤ −0.30) included changes in variables that assessed physical functioning, depression, anxiety, fatigue, and several variables related to impact on daily living. Independent predictor variables of endpoint PGI-I identified by stepwise linear regression included assessments for pain, physical function, vitality, anxiety, social function, and tender point thresholds. Conclusion. In addition to pain reduction, what makes patients with FM feel better may include improvement in fatigue, physical functioning, mood, and impact on daily living. An assessment of these domains may be important in clinical trials of FM and in the management of patients with FM.
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How this classification was reachedexpand
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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".