Abstract 178: Reporting of Imaging Time Intervals in patients with Acute Ischemic Stroke undergoing Perfusion Imaging: A Systematic review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Recent STIR guidelines stress the importance of speed and the need to balance the benefits of multi-modal imaging against potential treatment delay. It is essential that studies report on relevant imaging time intervals (ITIs) including image acquisition and interpretation times. We analyzed frequency of various ITIs reported. METHODS: The search strategy was conducted by combining the themes of acute ischemic stroke, perfusion imaging, and CT/MRI. Two independent reviewers screened at all levels and disagreements were settled through consensus. The inclusion criteria was CT/MR perfusion within 24 hours of stroke symptom onset and thresholds reporting for core, penumbra, and/or normal/not at risk tissue. We collected data on relevant a priori specified ITIs from each study (Table 1) and report these as medians (of either mean or median interval time in each individual study). RESULTS: The search resulted in 9184 abstracts from EMBASE, 7249 abstracts from MEDLINE resulting in 11919 abstracts after duplicates. Of 711 studies identified for full-text review, 94 studies reported at least one relevant time interval. Pooled estimates (medians with IQR) are reported for each ITI (Table 1). We noted lack of clarity on whether these ITIs were reporting beginning or end of image (CT or MRI) acquisition. Only 3/94 (3.4%) studies reported admission to imaging time. 54/134 (40%) studies reported image acquisition time; no studies reported on image interpretation times. CT acquisition (30 studies) ranged from 30-120 seconds with a median of 47 (41.25-58.75) seconds. MRI acquisition ranged from 46-144 seconds with a median of 69.50 (60.0-84.25) seconds. CONCLUSION: ITIs are significantly under-reported in studies that describe the use of multi-modal imaging (CT/MR perfusion). There exists significant heterogeneity in the definitions of many ITIs. Admission to end of imaging acquisition, an important marker of optimized workflow is minimally reported.
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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 | 0.036 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".