Utility of modified Rankin Scale for brain vascular malformations in hereditary hemorrhagic telangiectasia
Bibliographic record
Abstract
BACKGROUND: Approximately 10% of hereditary hemorrhagic telangiectasia (HHT) patients harbour brain vascular malformations (VMs). Intracranial hemorrhage (ICH) from brain VMs can lead to death or morbidity, while treatment options for brain VMs also have associated morbidity. The modified Rankin Scale (mRS) may provide an approach to identifying HHT-brain VM patients with poor outcomes, and their predictors. We aimed to measure the relationship between mRS score and brain VM, brain VM number, as well as other aspects of HHT, at enrollment and during prospective follow-up. METHODS: 1637 HHT patients (342 with brain VMs) were recruited from 14 HHT centres of the Brain Vascular Malformation Consortium since 2010 and followed prospectively (mean = 3.4 years). We tested whether the presence of brain VM, other HHT organ involvement, and HHT mutation genotype were associated with worse mRS scores at baseline and during follow-up, using linear mixed models, adjusting for age, sex, and year of visit. RESULTS: Presence of brain VMs was not associated with worse mRS score at baseline and there was no significant worsening of mRS with prospective follow-up in these patients; 92% had baseline mRS of 0-2. HHT-related gastrointestinal (GI) bleeding was associated with worse mRS scores at baseline (0.37, 95% CI 0.26-0.47, p < 0.001), as were history of anemia (0.35, 95% CI 0.27-0.43, p < 0.001) and liver VMs (0.19, 95% CI 0.09-0.30, p < 0.001). Presence of pulmonary arteriovenous malformations (AVMs) was not associated with worse mRS scores at baseline. mRS score was not associated with either HHT genotype (Endoglin vs ACVRL1). Only GI bleeding was associated with a significantly worsening mRS during prospective follow-up (0.64, 95% CI 0.21-1.08, p = 0.004). CONCLUSION: Most HHT-brain VM patients had good functional capacity (mRS scores 0-2) at baseline that did not change significantly over 3.4 mean years of follow-up, suggesting that mRS may not be useful for predicting or measuring outcomes in these patients. However, HHT patients with GI bleeding, anemia history or liver VMs had worse mRS scores, suggesting significant impact of these manifestations on functional capacity. Our study demonstrates the insensitivity of the mRS as an outcomes measure in HHT brain VM patients and reinforces the continued need to develop outcomes measures, and their predictors, in this group.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".