Linking process indicators and clinical/safety outcomes to assess the effectiveness of abatacept (<scp>ORENCIA</scp>) patient alert cards in patients with rheumatoid arthritis
Bibliographic record
Abstract
PURPOSE: Patient alert cards (PACs) for abatacept (ORENCIA) inform patients and healthcare professionals (HCPs) about the risk of infections and allergic reactions. The study evaluates the effectiveness of the PACs in rheumatoid arthritis patients and HCPs, using process indicators (awareness, receipt, utility, knowledge, behaviour) and outcomes. METHODS: Surveys of patients and HCPs in five European countries. A retrospective chart review permitted linking clinical and safety outcomes with survey responses. RESULTS: Data on 190 patients and 79 HCPs (50 physicians and 29 nurses) were analysed. Sixty percent of patients were aware of the PAC, of whom 95% had received it. Knowledge of risk of infection was higher among patients who had received the PAC vs those who had not (64% vs 46%; P = .013). Infections leading to hospitalisation increased with decreasing patient survey global scores: scores of ≥67%, 34%-67% and ≤ 33% were associated with hospitalisation rates of 2.5%, 5.2% and 8.4%, respectively (P = .4). Among HCPs 90% were aware and 68% had accessed the PAC. More nurses than physicians were aware (93% vs 88%), had accessed (78% vs 74%), read (90% vs 59%), distributed (81% vs 66%) and explained the content (94% vs 43%) of the PAC. Knowledge of risk of infection was higher among HCPs who had (91%) vs those who had not (73%) accessed the PAC (P = .053). CONCLUSIONS: PACs were effective in improving knowledge of key safety messages in patients and HCPs. This novel study design bridges the gap of linking process indicators with outcomes in the same patients, thereby strengthening the clinical relevance of patient surveys.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".