Evaluating the efficacy of an attention modification program for patients with fibromyalgia: a randomized controlled trial
Bibliographic record
Abstract
Persons with chronic musculoskeletal pain may be hypervigilant for pain-related cues which, paradoxically, may be maintaining their pain. Several randomized controlled trials have assessed whether a modified dot-probe protocol (ie, attention bias modification [ABM]) reduces chronic pain- and pain-related symptoms in persons with several diagnoses, including fibromyalgia. Scalability and economic efficiency potentiates the appeal of ABM protocols; however, research results have been mixed, with only some studies evidencing significant symptom gains from ABM and some evidencing gains for the control group. The current randomized controlled trial sought to replicate and extend previous ABM research using idiosyncratic word stimuli and a 1-month follow-up. Participants included treatment-seeking adult women (n = 117) with fibromyalgia who were randomly assigned to a standard (ie, control) or active (ie, ABM) condition. The protocol was delivered online and involved twice-weekly 15-minute sessions, for 4 weeks, with questionnaires completed at baseline, posttreatment, and 1-month follow-up. Symptom reports were analysed with mixed hierarchical modelling. There was no evidence of differences between the control and ABM groups. Both groups had small significant (Ps < 0.05) improvements in pain experiences at posttreatment, but not at follow-up (Ps > 0.05). There were no significant changes for either group on measures of anxiety sensitivity, illness/injury sensitivity, pain-related fear, pain-related anxiety, or attentional biases (Ps > 0.05). The current findings add to the emerging and mixed literature regarding ABM for pain by demonstrating that ABM produces no substantive improvements in pain or pain-related constructs in a large sample of patients with fibromyalgia.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".