Factors affecting pre- and post-stenting computed tomography perfusion in patients with middle cerebral artery stenosis
Bibliographic record
Abstract
The aim of this study was to investigate the factors affecting pre- and post-stenting head computed tomography perfusion (CTP) in patients with middle cerebral artery stenosis. A total of 25 patients with severe middle cerebral artery stenosis were enrolled. CTP was performed prior to and following stenting. Scores were allocated to the time-to-peak (TTP) parameter of CTP using the Alberta stroke program early computed tomography scoring (ASPECTS) scale. The factors possibly affecting pre- and post-stenting CTP were analyzed. All the patients exhibited markedly prolonged TTP on the affected side prior to stenting, compared with the healthy side. Following surgery, the TTP was improved in all patients. The preoperative ASPECTS score was negatively correlated with the degree of middle cerebral artery stenosis with a correlation coefficient of -5.78. The preoperative vascular stenosis rate was positively correlated with the improvement degree of the ASPECTS score with a correlation coefficient of 1.137 (P=0.001). TTP is a sensitive parameter for evaluating the effect of stenting on middle cerebral artery stenosis. TTP prior to and following stenting may be quantitatively assessed using the ASPECTS scale. Patients with serious stenosis and/or good collateral circulation are able to benefit more from stenting.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".