International development and validation of a multilingual bank of items for the self-assessment of essential knowledge by systemic lupus erythematosus patients: the SLE Knowledge Assessment score (SLAKE)
Bibliographic record
Abstract
OBJECTIVES: Patient education is increasingly acknowledged as an important aspect of the management of systemic lupus erythematosus (SLE). The aim of the study was to develop the SLE Knowledge Assessment score (SLAKE), a digital multilingual self-assessment tool designed to quantify essential SLE knowledge. METHODS: International healthcare professionals (HCPs) and patient representatives engaged in a multi-step process to: identify essential SLE knowledge domains, select key domains via rating, and generate an item bank of 394 questions across 11 domains, which was then adapted into 19 languages. For validation, participants completed 44 questions (including 33 randomly selected), with scores calculated for total knowledge and the 11 specific domains. Statistical analyses examined associations between scores and demographic, clinical, and educational variables. RESULTS: SLAKE was used by 1182 SLE participants (1120 [94.8%] women, median age: 45 years [IQR: 35-54 years]), with a median SLE duration of 10 years (IQR: 4-20 years). The median SLAKE score was 37 (IQR: 34-40) of a maximum of 44 points while the median score across the 11 SLAKE domains ranged between 3 and 4 over a maximum of 4 points. There was a significant positive association between SLAKE score and SLE duration (p= 0.006), previous participation to a patient education course or a patient training for lupus (p< 0.0001) and the education level (p< 0.0001) but not with age (p= 0.48) or gender (p= 0.39). CONCLUSION: SLAKE is a valid, multilingual, digital self-assessment tool that effectively measures essential SLE knowledge. Its randomized question bank and domain-specific scoring enable targeted education, ultimately supporting better disease management.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".