A dual-center validation of the PIRAMD scoring system for assessing the severity of ischemic Moyamoya disease
Bibliographic record
Abstract
Background: Prior Infarcts, Reactivity, and Angiography in Moyamoya Disease (PIRAMD) is a recently proposed imaging-based scoring system that incorporates the severity of disease and its impact on parenchymal hemodynamics in order to better support clinical management and evaluate response to intervention. In particular, PIRAMD may have merit in identifying symptomatic patients that may benefit most from revascularization. Our aim was to validate the PIRAMD scoring system. Methods: Patients with ischemic Moyamoya disease, who underwent catheter angiographic [modified Suzuki Score (mSS) and collateralization status], morphological MRI and a parenchymal hemodynamic evaluation with blood oxygenation-level dependent cerebrovascular reactivity (BOLD-CVR) at two transatlantic centers, were retrospectively included. The primary outcome was the presence of neurological symptoms. The diagnostic capacity of each PIRAMD feature alone was evaluated, as well as combined and the inter-institutional differences of each parameter were evaluated. Results: Seventy-two hemispheres of 38 patients were considered for analysis, of which 39 (54%) were classified as symptomatic. The presence of a prior infarct had the highest odds ratio [odds ratio (OR) =24; 95% CI: 6.7-87.2] for having neurological symptoms, followed by impaired CVR (OR =17; 95% CI: 5-62). No inter-institutional differences in the odds ratios or area under the curve (AUC) were found for any study parameter. The PIRAMD score had an AUC of 0.88 (95% CI: 0.80-0.96) with a similar AUC for the PIRAMD grading score. Conclusions: Our multicentric validation of the recently published PIRAMD scoring system was highly effective in rating the severity of ischemic Moyamoya disease with excellent inter-institutional agreement. Future studies should investigate the prognostic value of this novel imaging-based score in symptomatic patients with Moyamoya disease.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".