A Discriminative Prediction Model of Neurological Outcome for Patients Undergoing Surgery of Brain Arteriovenous Malformations
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To develop and validate a discriminative model for predicting neurological morbidity after brain arteriovenous malformation (bAVM) surgery. METHODS: Of 233 consecutive, prospectively enrolled patients undergoing bAVM surgery, the first 175 were used to derive, and the last 58 to validate, the prediction model. Demographic and angiographic factors were related to modified Rankin Scale scores assigned before, within 72 hours, at 7 days and at > or =1 year after surgery to seek predictors of postoperative neurological deficits (modified Rankin Scale score > or =3). These factors included nidus size, eloquence, venous drainage, diffuseness, white matter configuration, arterial perforator supply and associated aneurysms. RESULTS: Brain eloquence, diffuse nidus and deep venous drainage were significant predictors of early disabling neurological deficits (odds ratios of 4.33, 3.49 and 2.38, respectively). The rounded odds ratios form a weighted 9-point prediction model (maximum scores for eloquence+diffuseness+deep drainage=4+3+2). The score discriminated the probability of experiencing both early (first week) and permanently (at > or =1 year) disabling neurological deficits as follows: 0 to 2: 1.8%, 3 to 5: 17.4%, 6 to 7: 31.6%, >7: 52.9% for early and 0 to 2: 1.8%, 3 to 5: 4.4%, 6 to 7: 18.4%, >7: 32.4% for permanently disabling outcomes. The discrimination of the model was 0.80 with 2.8% optimism. Validation in the second patient cohort revealed good performance at risk stratification. CONCLUSIONS: Relative weights assigned to brain eloquence, diffuse nidus morphology and deep venous drainage of a bAVM provide a simple and discriminative prediction model for neurological outcome after bAVM surgery.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".