Best-practice Indicators in Psoriatic Disease Care
Bibliographic record
Abstract
OBJECTIVE: In 2016, members of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA), in collaboration with KPMG LLP (UK), conducted a study to measure care in psoriatic arthritis (PsA). A key finding was that centers do not usually have processes in place to measure the effect of improved quality of care. Our objectives were to identify and select best-practice indicators to enable PsA caregivers to assess and monitor the outcomes of specific initiatives aimed at improving care in 4 focus areas: (1) shortening time to diagnosis; (2) improving multidisciplinary collaboration; (3) optimizing disease management; and (4) improving disease monitoring. METHODS: (1) Structured review of scientific and grey literature to obtain evidence for a long list of 100 potential indicators across the 4 focus areas; (2) survey expert rheumatologists and dermatologists to review the long list and identify the most meaningful and feasible indicators for use in day-to-day practice; (3) consensus discussion to identify a shortlist of indicators based on predefined selection criteria; (4) electronic group discussion to refine definitions of shortlisted indicators and targets; and (5) review of the shortlisted indicators at the annual GRAPPA meeting in July 2018 to ensure the indicators meet the preliminary criteria. RESULTS: The expert group arrived at a consensus with a shortlist of 8 best-practice indicators across 4 key focus areas aligned with the patient pathway. CONCLUSION: There were 8 evidence-based best-practice indicators and respective targets that were identified to enable the monitoring of quality of care and target improvements.
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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 | 0.125 | 0.307 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".