Protocol for a multicentre randomised controlled parallel-group trial to compare the effectiveness of remotely delivered cognitive-behavioural and graded exercise interventions with usual care alone to lessen the impact of fatigue in inflammatory rheumatic diseases (LIFT)
Bibliographic record
Abstract
INTRODUCTION: Fatigue remains pervasive, disabling and challenging to manage across all inflammatory rheumatic diseases (IRDs). Non-pharmacological interventions, specifically cognitive-behavioural approaches (CBAs) and graded exercise programmes designed to support and increase exercise, are valuable treatments which help patients with IRD to manage their fatigue. Yet, healthcare systems have encountered substantial barriers to the implementation of these therapeutic options. Lessening the Impact of Fatigue in Inflammatory Rheumatic Diseases: a Randomised Trial (LIFT) is designed to give insights into the effectiveness of a remotely delivered standardised intervention for a range of patients with IRD. It will also enable the exploration of putative moderating factors which may allow for the future triage of patients and to investigate the precise mediators of treatment effect in IRD-related fatigue. METHODS AND ANALYSIS: LIFT is a pragmatic, multicentre, three-arm randomised, controlled trial, which will test whether adapted CBA and personalised exercise programme interventions can individually reduce the impact and severity of fatigue. This will be conducted with up to 375 eligible patients diagnosed with IRD and interventions will be delivered by rheumatology healthcare professionals, using the telephone or internet-based audio/video calls. ETHICS APPROVAL AND DISSEMINATION: Ethical approval has been granted by Wales REC 7 (17/WA/0065). Results of this study will be disseminated through presentation at scientific conferences and in scientific journal. A lay summary of the results will be sent to participants. TRIAL REGISTRATION NUMBER: NCT03248518; Pre-results.
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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 | 0.052 | 0.054 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.136 | 0.032 |
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".