Predicting functional outcome after stroke by modelling baseline clinical and CT variables
Bibliographic record
Abstract
BACKGROUND: we aimed to assess whether the performance of stroke outcome models comprising simple clinical variables could be improved by the addition of more complex clinical variables and information from the first computed tomography (CT) scan. METHODS: 538 consecutive acute ischaemic and haemorrhagic stroke patients were enrolled in a Stroke Outcome Study between 2001 and 2002. Independent survival (modified Rankin scale <or=2) was assessed at 6 months. Models based on clinical and radiological variables from the first assessment were developed using multivariate logistic regression analysis. RESULTS: three models were developed (I-III). Model I included age, pre-stroke independence, arm power and a stroke severity score (area under a receiver operating characteristic curve, AUC = 0.882) but performed no better than Model II, which comprised age, pre-stroke independence, normal verbal component of the Glasgow coma score, arm power and being able to walk without assistance (AUC 0.876). Model III, including two radiological variables and clinical variables, was not statistically superior to model II (AUC 0.901, P = 0.12). Model II was externally validated in two independent datasets (AUCs of 0.773 and 0.787). CONCLUSION: this study demonstrates an externally validated stroke outcome prediction model using simple clinical variables. Outcome prediction was not significantly improved with CT-derived radiological variables or more complex clinical variables.
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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 | 0.001 | 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".