Educational Bias in Cognitive Screening of Adults with Sickle Cell Disease: A Bilingual Multisite Observational Study
Bibliographic record
Abstract
Cognitive impairment is a common and dreaded complication of sickle cell disease (SCD), profoundly affecting patients’ quality of life, education, and employment. Despite its significance, there is a striking lack of guidance on optimal screening strategies, with existing tools often skewed by biases related to language proficiency and educational background, leaving many patients undiagnosed and unsupported. The Rowland Universal Dementia Assessment Scale (RUDAS) was specifically designed for cognitive screening in multicultural populations. We hypothesized that in adults with SCD, RUDAS performance is less influenced by educational attainment when compared to the Montreal Cognitive Assessment (MoCA). We conducted a cross-sectional study of adults with SCD who underwent cognitive screening at the Henri-Mondor Hospital using RUDAS and MoCA. Educational attainment was scored as the years of schooling for the highest completed diploma (HLE). Abnormal RUDAS (<28) and MoCA (<26) scores were found in 55/73 (75.3%) and 52/73 (71.2%). Both scores increased significantly with HLE (p < 0.001). Adding 1 point for those with the HLE < 12 years significantly mitigated the effect of education on RUDAS but only partially for MoCA (p = 0.26 and p = 0.003). In an independent cohort of 252 adults, this adjustment for HLE significantly lessened the effect of education on RUDAS. These results suggest there is an educational bias in neurocognitive screening of adults with SCD. We propose that the RUDAS adjusted for HLE is a promising novel strategy to systematically identify those in need of comprehensive neurocognitive assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".