External validation of a six simple variable model of stroke outcome and verification in hyper-acute stroke
Bibliographic record
Abstract
We aimed to validate a previously described six simple variable (SSV) model that was developed from acute and sub-acute stroke patients in our population that included hyper-acute stroke patients. A Stroke Outcome Study enrolled patients from 2001 to 2002. Functional status was assessed at 6 months using the modified Rankin Scale (mRS). SSV model performance was tested in our cohort. 538 acute ischaemic (87%) and haemorrhagic stroke patients were enrolled, 51% of whom presented to hospital within 6 h of symptom recognition. At 6 months post-stroke, 42% of patients had a good outcome (mRS < or = 2). Stroke patients presenting within 6 h of symptom recognition were significantly older with higher stroke severity. In our Stroke Outcome Study dataset, the SSV model had an area under the curve of 0.792 for 6 month outcomes and performed well for hyper-acute or post-acute stroke, age < or > or = 75 years, haemorrhagic or ischaemic stroke, men or women, moderate and severe stroke, but poorly for mild stroke. This study confirms the external validity of the SSV model in our hospital stroke population. This model can therefore be utilised for stratification in acute and hyper-acute stroke trials.
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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 | 0.046 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".