Is This Contrast? Is This Blood? An Agreement Study on Post-thrombectomy Computed Tomography Scans
Bibliographic record
Abstract
Background: Hemorrhagic transformation after acute ischemic stroke is a dreaded and severe complication of thrombolysis and thrombectomy. However, its detection on post-thrombectomy conventional non-contrast computed tomography (CT) scan can be complicated by the frequent (and sometimes concomitant) presence of contrast, resulting in changes in management. Aims: Our objective was to assess the inter- and intra-rater reliability for the detection of blood and/or contrast on day-1 post-thrombectomy CT scans. Methods: A total of 18 raters across 3 different specialties independently examined 30 post-thrombectomy CT scans selected from the Aspiration vs. STEnt-Retriever (ASTER) trial. They were asked to judge the presence of blood and contrast. Thirty days later, the same 18 raters again independently judged the 30 scans, in randomized order. Agreement was measured with Fleiss' and Cohen's K statistics. Results: Overall agreement on blood and/ or contrast presence was only fair, k = 0.291 (95% CI = 0.273–0.309). There were 0 scans with consensus among the 18 readers on the presence of blood and/or contrast. However, intra-rater global agreement across all 18 physicians was relatively high, with a median kappa value of 0.675. This intra-rater consistency was seen across all specialties, regardless of level of training. Conclusion: Physician judgment for the presence of blood and/or contrast on day-1 post-thrombectomy non-contrast CT scan shows limited inter-observer reliability. Advanced imaging modalities may then be warranted for challenging clinical cases.
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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 | 0.044 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".