Is the Madrid Sonographic Enthesitis Index Useful for Differentiating Psoriatic Arthritis from Psoriasis Alone and Healthy Controls?
Bibliographic record
Abstract
OBJECTIVE: To assess the usefulness of the MAdrid Sonographic Enthesitis Index (MASEI) in classifying patients as having psoriatic arthritis (PsA) and comparing entheseal abnormalities between patients with PsA, psoriasis alone (PsC), and healthy controls (HC). METHODS: Patients with PsC were assessed to exclude inflammatory arthritis. The MASEI scoring system was used to quantify the extent of ultrasonographic (US) entheseal abnormalities. The total MASEI score was categorized into items that reflected inflammatory abnormalities (MASEI-inflammatory) and chronic damage (MASEI-damage). Nonparametric tests were used to compare MASEI scores across the groups. A cutoff point of MASEI ≥ 20 was used to calculate the sensitivity and specificity of the MASEI to classify patients as having PsA. RESULTS: Patients with PsA (n = 50), PsC (n = 66), and HC (n = 60) were assessed. Total MASEI scores were higher in patients with PsA than in those with PsC, and both those groups were higher than HC (p < 0.0001). MASEI-inflammatory showed a similar trend (p < 0.0001). MASEI-damage was higher in patients with PsA compared to both patients with PsC and HC (p < 0.0001); however, no difference was observed between patients with PsC and HC. No significant difference in MASEI scores was found across the 3 groups in patients with a body mass index > 30. The sensitivity of the MASEI score to correctly classify patients as having PsA was 30% and the specificity was 95% when compared to HC and 89% when compared to PsC. CONCLUSION: The severity of US entheseal abnormalities is highest in patients with PsA followed by PsC and is lowest in healthy controls. MASEI can specifically classify patients as having PsA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".