Validation of the Spanish, Portuguese and French versions of the Lupus Damage Index questionnaire: data from North and South America, Spain and Portugal
Bibliographic record
Abstract
We have previously developed and validated a self-administered questionnaire, modelled after the Systemic Lupus International Collaborating Clinics Damage Index (SDI), the Lupus Damage Index Questionnaire (LDIQ), which may allow the ascertainment of this construct in systemic lupus erythematosus (SLE) patients followed in the community and thus expand observations made about damage. We have now translated, back-translated and adapted the LDIQ to Spanish, Portuguese and French and applied it to patients followed at academic and non-academic centres in North and South America, Portugal and Spain while their physicians scored the SDI. A total of 887 patients (659 Spanish-speaking, 140 Portuguese-speaking and 80 French-speaking patients) and 40 physicians participated. Overall, patients scored all LDIQ versions higher than their physicians (total score and all domains). Infrequent manifestations had less optimal clinimetric properties but overall agreement was more than 95% for the majority of items. Higher correlations were observed among the Spanish-speaking patients than the Portuguese-speaking and French-speaking patients; further adjustments may be needed before the Portuguese and French versions of the LDIQ are applied in community-based studies. The relationship between the LDIQ and other outcome parameters is currently being investigated in a different patient sample.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".