Abstract TP417: An Autoregulation-Derived Prediction Model for Midline Shift After Ischemic Stroke
Bibliographic record
Abstract
Introduction: Identification of patients likely to develop midline shift (MLS) after large-vessel occlusion (LVO) stroke is essential for appropriate triage and patient disposition. Studies have identified clinical and radiographic predictors of MLS, but with limited accuracy. Using an innovative assessment of cerebral autoregulation, we sought to develop an accurate predictive model for MLS. Methods: We prospectively enrolled 73 patients with LVO stroke. Beat-by-beat cerebral blood flow (transcranial Doppler) and arterial pressure (arterial catheter or finger photoplethysmography) were recorded within 24 hours of the stroke, and a 24-hour brain MRI was obtained to determine infarct volume and MLS. Autoregulatory function was quantified from pressure-flow relation via projection pursuit regression (PPR), allowing for characterization of 5 hemodynamic markers (Figure 1A). We assessed the predictive relation of autoregulatory capacity and radiological and clinical variables to MLS using recursive classification tree models. Results: PPR successfully quantified autoregulatory function in 50/73 (68.5%) patients within 24 hours of LVO ischemic stroke (age 63.9±13.6, 66% F, NIHSS 15.8±6.7). Of these 50 patients, most (78%) underwent endovascular therapy. Thirteen (26%) experienced 24-h MLS; in these patients, infarct volumes were larger (140.2 vs. 48.6 mL, P<0.001 ), and ipsilateral (but not contralateral) falling slopes were steeper (1.1 vs. 0.7 cm·s -1 ·mmHg -1 , P=0.001 ). Among all clinical, demographic, and hemodynamic variables, only two (infarct volume, ipsilateral falling slope) significantly contributed to prediction of MLS (accuracy 94%; Figure 1B). Conclusions: This predictive model of MLS wields translatable potential for triaging level of care in patients suffering from LVO ischemic stroke, but further research, including optimization of the PPR algorithm as well as prospective use of the predictive model, is needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".