Processing and Interpretation Times of CT Angiogram and CT Perfusion in Stroke
Bibliographic record
Abstract
OBJECTIVE: To determine the mean time for acquiring computed tomogram perfusion (CTP) and CT angiogram (CTA) images in acute stroke. To determine and compare processing and interpretation times amongst three groups of radiologists with varying degree of expertise: two radiology residents (Group I), two neuroradiology fellows (Group II) and four consultant neuroradiologists (Group III). METHODS: The mean time of acquisition of CTA and CTP studies was calculated among ten patients presenting with acute stroke. All readers had to process the CTA and CTP images, interpret them (for presence or absence of thrombus and penumbra) and save them on the GE Advantage Windows workstation. The mean time for processing and interpreting these studies was calculated. RESULTS: The mean time for acquisition of CTA and CTP studies in the ten patients was 14.6 +/- 5.9 minutes. The time taken for CTA processing and interpretation in Groups I, II and III was 2.3 +/- 1.3 min, 1.6 +/- 0.4 min and 1.5 +/- 0.7 min respectively. The time required for CTP processing and interpretation by the same groups was 5.2 +/- 1.7 min, 4.5 +/- 1.5 min and 4.1 +/- 1.1 min respectively. There was a statistically significant difference of means between Groups I and III in the CTA and CTP processing and interpretation times (p=0.02, p=0.01 respectively) but no statistical difference between Groups I and II (p=0.15, p=0.22 respectively) or Groups II and III (p=0.31, p=0.30 respectively). CONCLUSION: The CTA and CTP studies can be performed, processed and interpreted quickly in acute stroke.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".