Mild cognitive impairment in psoriatic arthritis
Bibliographic record
Abstract
ABSTRACT: To assess the prevalence and factors associated with mild cognitive impairment (MCI) in patients suffering from psoriatic arthritis (PsA).A cross-sectional evaluation was conducted in consecutive PsA patients. Sociodemographic data and the clinimetric variables related to PsA and psoriasis were collected for each patient. MCI was assessed through the Montreal Cognitive Assessment (MoCA). The cognitive performance of PsA patients was compared to healthy subjects using one-way analysis of variance (ANOVA). The correlations among variables were studied by the Spearman rank correlation coefficient. A multivariate logistic regression analysis was carried out to establish the predictors of MCI.The study involved 96 PsA patients and 48 healthy subjects. MCI (defined as a MoCA score < 26/30) was detected in 47 (48.9%) PsA patients. Compared to healthy subjects, the MoCA score resulted significantly lower in PsA patients (P = .015). The main differences involved the denomination and language domains. MoCA was negatively correlated with age (r = -0.354; P < .0001), HAQ-DI (r = -0.227; P = .026), and fatigue (r = -0.222; P = .029), and positively correlated with psoriasis duration (r = 0.316; P = .001) and DLQI (r = 0.226; P = .008).The multivariate logistic regression analysis revealed the duration of psoriasis (P = .0005), age (P = .0038), PASI (P = .0050), and HAQ-DI (P = .0193) as predictors of the MoCA score.MCI is present in a significant proportion of PsA patients, and is mainly determined by age, cutaneous variables, and disability.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".