Abstract WP53: 24-hour Infarct Volume on Non-contrast CT as a Predictor of Functional Outcome at 90 days
Bibliographic record
Abstract
Introduction: Reliable early stroke prognostication has important implications for goals of care planning, treatment guidance, and disposition planning. At present, prediction of patient long term functional outcomes remains challenging. Hypothesis: Infarct volumes on early non-contrast CT may be a useful tool in predicting long term stroke functional outcomes. Methods: Non-contrast CT images from 557 patients in the ALIAS2 trial were collected. Infarct and hemorrhage volumes at 24hrs post treatment were measured by 4 investigators blinded to patient clinical information. Abnormalities were outlined manually on each image slice and final volumes were calculated using Quantamo 1.0, a volumetric analysis tool. The location and volume of the acute abnormalities were correlated with modified Rankin Scales (mRS) scores at 90-day follow-up using Pearson correlation coefficients. Results: There is a strong correlation between infarct volumes measured on CT at 24 hours with functional outcomes measured at 90 days r = 0.465 with every 18.6ml of brain infarct contributing to 1 point increase in mRS. Infarct volumes also correlated with number of hospital stays, patient death, and initial assessments using Alberta Stroke Program Early CT Score r= 0.211, 0.395 and -0.377 respectively. Acute hemorrhage volume correlated strongly with poor functional outcomes with 8.2ml of hemorrhage corresponding to 1 point increase in mRS r =0.468. A comparison of patients treated with placebo vs. high dose albumin showed no statistically significant difference in infarct volumes. Conclusions: Measurement of brain infarction on CT at 24 hours post treatment can predict long-term functional outcomes and maybe a useful tool in guiding management.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".