A practical approach to neuroimaging in stroke
Bibliographic record
Abstract
This chapter, ‘A practical approach to neuroimaging in stroke’, provides an overview on the different imaging modalities used in stroke medicine, including a brief outline of more advanced research tools. There is a recap of imaging used in acute infarction, the Alberta Stroke Programme Early CT Score (ASPECTS), computed tomography (CT) perfusion, magnetic resonance (MR) perfusion, arterial spin labelling, magnetic resonance spectroscopy; subacute imaging as in haemorrhagic transformation of infarcts, fogging effect, contrast enhancement; direct plaque imaging, and ultrasonography. Finally, it explores the dedicated vascular imaging techniques used for assessing the aetiology of stroke and guiding management. Due to the time-critical nature of treatment in stroke, it is important that imaging is readily accessible. With the multitude of imaging modalities and studies available, it is important that clinicians have sufficient knowledge to be able to select the most appropriate test for the patient, and to be aware of potential limitations and diagnostic pitfalls.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.025 |
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".