Consensus Approach to a Treat-to-target Strategy in Juvenile Idiopathic Arthritis Care: Report From the 2020 PR-COIN Consensus Conference
Bibliographic record
Abstract
OBJECTIVE: Treat to target (T2T) is a strategy of adjusting treatment until a target is reached. An international task force recommended T2T for juvenile idiopathic arthritis (JIA) treatment. Implementing T2T in a standard and reliable way in clinical practice requires agreement on critical elements of (1) target setting, (2) T2T strategy, (3) identifying barriers to implementation, and (4) patient eligibility. A consensus conference was held among Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN) stakeholders to inform a statement of understanding regarding the PR-COIN approach to T2T. METHODS: PR-COIN stakeholders including 16 healthcare providers and 4 parents were invited to form a voting panel. Using the nominal group technique, 2 rounds of voting were held to address the above 4 areas to select the top 10 responses by rank order. RESULTS: Incorporation of patient goals ranked most important when setting a treatment target. Shared decision making (SDM), tracking measurable outcomes, and adjusting treatment to achieve goals were voted as the top elements of a T2T strategy. Workflow considerations, and provider buy-in were identified as key barriers to T2T implementation. Patients with JIA who had poor prognostic factors and were at risk for high disease burden were leading candidates for a T2T approach. CONCLUSION: This consensus conference identified the importance of incorporating patient goals as part of target setting and of the influence of patient stakeholder involvement in drafting treatment recommendations. The network approach to T2T will be modified to address the above findings, including solicitation of patient goals, optimizing SDM, and better workflow integration.
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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 | 0.308 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.010 | 0.022 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".