The prediction of response to treatment using power Doppler in rheumatoid arthritis: a systematic review
Bibliographic record
Abstract
Objectives: There is no consensus on how musculoskeletal ultrasound (US), especially power Doppler (PD) positivity, should inform treatment decisions in RA. We aimed to summarize the literature on whether PD positivity can predict response to intensification of RA therapies in patients with moderate to high clinical disease activity. Methods: A systematic literature review was performed using a predefined PICO strategy. The titles and abstracts, and subsequently full texts, were independently screened and reviewed by two reviewers and any disagreement was resolved by a third investigator. Studies that investigated the predictive value of PD were included. Results: Among 2580 abstracts/titles, 13 studies were included. Studies were heterogeneous regarding the inclusion criteria, baseline and new treatments, scanned joints and follow-up duration. In eight studies, patients with higher baseline PD activity had a better response to treatment, mostly with higher reductions in clinical indices. In contrast, two studies found that the probability of achieving clinical remission decreased as the baseline PD score increased. There was no association between baseline PD and the achievement of clinical remission at the follow-up in the remaining three studies. Conclusion: The baseline Doppler severity may suggest better improvement with higher reductions in composite scores, but this may not be enough to predict a remission state. How US can be used to predict response in RA management requires a well-designed study that will need to be shaped by the existing observations, most importantly identifying the outcome measure that will also be important for daily practice.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".