CT Perfusion Spot Sign Improves Sensitivity for Prediction of Outcome Compared with CTA and Postcontrast CT
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recent studies have recommended both early and late imaging to increase spot sign detection. However optimal acquisition timing for spot detection and impact on outcome prediction is uncertain. Our aim was to assess the utility of CTP in spot sign detection and characterization with emphasis on its impact on the prediction of outcome in patients with acute primary ICH. MATERIALS AND METHODS: A retrospective review of 28 patients presenting within 6 hours of ICH, studied with CTA, CTP, and postcontrast CT, was performed. CTA, CTP, and postcontrast CT spot sign characteristics were recorded according to predefined radiologic criteria. A combined primary outcome of hematoma expansion or poor clinical outcome was used and defined as hematoma expansion ≥6 mL or ≥30%, need for surgical drainage, or in-hospital mortality. Associations with the primary outcome and spot sign presence were examined against baseline clinical, laboratory, and radiographic variables. Predictive ability of CTA, CTP, and postcontrast CT spot characteristics were compared among modalities. RESULTS: Primary outcome criteria were met in 18 patients (61%). CTP spot sign presence was an independent predictor of hematoma expansion or poor outcome (P = .040) and demonstrated greater sensitivity (78%) than spots detected on CTA (44%, P = .034) and postcontrast CT (50%, P = .025). Specificity and positive predictive value of the spot sign was high (100%) on all modalities. CTP detected the greatest number of spots (80%) with peak spot attenuation demonstrated at a median (interquartile range) time of 50 seconds (range, 34-63 seconds) after contrast bolus injection. CTP spot appearance was later than CTA-detected spots (P = .002) and earlier than postcontrast CT spots (P < .001). CONCLUSIONS: CTP spot sign detection improves the sensitivity for prediction of outcome compared with CTA or postcontrast CT-detected spots.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".