Achieving consensus on quality indicators (QI) for pediatric Systemic Lupus Erythematosus (pSLE)
Bibliographic record
Abstract
Background QI are retrospectively measurable elements of practice performance for which there is evidence or consensus that can be used to assess the quality of care provided. Aim To develop a set of consensus-derived QI for pSLE to serve as international benchmarks for the quality of patient care. Methods Based on the medical literature a Delphi survey was created and distributed to the physician membership of PRES, PANLAR, CARRA and the ACR via e-mail. Consensus was considered 80% or higher. Results There was consensus (97%) among the 297 respondents that simply applying QI developed by the ACR and EULAR for adults with SLE (adult QI) was insufficient and that distinct QI for pSLE were needed. Respondents concurred that 5 of the 20 ACR and 6 of the 24 EULAR adult QI are also suitable for pSLE. An additional 14 ACR and 13 EULAR adult QI might be useful for pSLE after modifications. There was no consensus whether to consider “Pregnancy” (45%) and “Reproductive Health” (65%) as domains in the set of pSLE QI. Conclusion There is great demand among pediatric rheumatologists to develop QI for pSLE. Initial agreement has been reached about the types and domains of QI for pSLE, but additional discussion and consensus formation under consideration of the medical evidence is needed to finalize a set of QI for pSLE that can be used to define standard of care treatment for children and adolescents with pSLE.
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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 | 0.315 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".