Developing predictive models of excellent and devastating outcome after stroke
Bibliographic record
Abstract
BACKGROUND: models to predict functional status post-stroke have utility in balancing groups in randomised trials, for outcome comparison between stroke centres and may assist in outcome prediction. This study aimed to develop models of both excellent [modified Rankin score (mRS) 0-1] and devastating outcomes (mRS of 5-6). METHODS: patients admitted with ischaemic or haemorrhagic stroke in 2001-02 to the Halifax Infirmary, Canada, were enrolled. Sixteen clinical variables from the first neurological assessment and six radiological variables from the acute CT scan were used to the model outcome at 6 months. RESULTS: five hundred and thirty-eight stroke patients were enrolled. Thirty per cent had an excellent outcome and 30% had a devastating outcome. Three models of the excellent outcome were developed [area under the receiver operator curve (AUC) 0.866-882] including the variables age, pre-stroke functional status, stroke severity, ability to lift both arms, walk independently, normal verbal Glasgow Coma Scale and leukoaraiosis. Predictive models of the devastating outcome (AUC of 0.859-0.874) included additional variables living alone pre-stroke and total anterior circulation stroke. The simplest models of both outcomes were externally validated (AUC of 0.856-0.885). CONCLUSION: this study demonstrates new externally validated predictive models of both excellent and devastating outcomes. Leukoaraiosis was the only independent radiological predictor of both outcomes. Living alone pre-stroke predicted devastating outcome post-stroke.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".