Impact of flat-panel detector CT distal occlusion tracker (DOT) sign on early neurological deterioration after endovascular thrombectomy
Bibliographic record
Abstract
BACKGROUND: A relevant proportion of patients experience early neurological deterioration (END) despite technically successful recanalisation for acute ischaemic stroke. We prospectively assessed the distal occlusion tracker (DOT) sign, detectable on flat-panel detector CT (FPDCT) immediately after mechanical thrombectomy (MT), to identify distal embolisation or incomplete microvascular reperfusion associated with END. METHODS: Our prospective multicentre observational study included consecutive patients with anterior circulation stroke treated with MT between January 2022 and December 2023 at two large comprehensive stroke centres. Post-procedural FPDCT was used to assess the presence of the DOT sign. The primary outcome was END. Secondary outcomes included 3 month functional outcome, 24 hour Alberta Stroke Program Early CT Score (ASPECTS) and haemorrhagic complications. Associations between the DOT sign and clinical-radiological variables, including Thrombolysis in Cerebral Infarction recanalization score (TICI), were evaluated through univariate and multivariate logistic regression. RESULTS: The DOT sign was present in 31% of cases and was associated with higher rates of END (25% vs 12.8%, p=0.003), lower recanalisation success (79.3% vs 91.1%, p<0.001) and greater prevalence of cortical hyperattenuation. On multivariate analysis, independent predictors of END included the DOT sign (aOR=2.07, p=0.040), cardioembolic aetiology, baseline National Institutes of Health Stroke Scale, FPDCT ASPECTS and unsuccessful reperfusion. The DOT sign led to reclassification of half of TICI 3 cases to lower grades. No significant difference in symptomatic intracranial haemorrhage was observed between groups. CONCLUSIONS: The DOT sign is a practical post-thrombectomy imaging marker helping in the prediction of END. Integrating the DOT sign assessment may help in the stratification of tissues at risk and risk of END, adding to the selection of patients for adjunctive intra-arterial medications.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".