Bibliographic record
Abstract
Abstract The management of acute ischemic stroke has witnessed a paradigm change in the last few years with the advent of mechanical thrombectomy. Imaging plays a key role in evaluation and patient selection. Computed tomography (CT) forms the workhorse in most centers due to its widespread availability and quick performance, though magnetic resonance imaging (MRI) can also be adopted as a reasonable alternative. The key role of imaging is to rule out hemorrhage and other stroke mimics while at the same time establish early signs of ischemia and provide detailed information of cervicocranial vasculature and salvageable brain parenchyma; all in the shortest timeframe. Key imaging predictors of good clinical outcomes are good Alberta stroke protocol early CT score (ASPECTS) (greater than 6) and collateral scores. Selection of patients beyond the standard window period of 6 to 8 hours has become possible by tissue perfusion imaging with some recent trials demonstrating the utility of thrombectomy even up to 24 hours. Quick MRI-based protocols are being devised to achieve similar information as on CT with no adverse effects related to radiation and contrast effects. Research is underway to decipher the intricacies of blood flow in the brain through more sophisticated imaging methods in attempt to increase the base for mechanical thrombectomy, which will benefit more number of patients.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".