Importance of Reperfusion Status after Intra-Arterial Thrombectomy for Prediction of Outcome in Anterior Circulation Large Vessel Stroke
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Reperfusion status after intra-arterial thrombectomy (IAT) is a critical predictor of functional outcome after acute ischemic stroke. However, most prognostic models have not included a detailed assessment of reperfusion status after IAT. OBJECTIVE: The aim of this work was to assess the association between successful reperfusion and clinical outcome. METHODS: Clinical, radiological, and procedural variables of patients treated with IAT were extracted from our prospective stroke registry. The association with functional outcome using the modified Rankin Scale (mRS) after 3 months was assessed using multivariable logistic regression. An extension of the modified TICI score, eTICI, was used to classify reperfusion status. The prognostic value of reperfusion status after IAT in addition to age, stroke severity, imaging characteristics, treatment with intravenous thrombolysis, and time from symptom onset to the end of IAT was assessed with logistic regression and summarized with receiver operating characteristic curves. RESULTS: In total, 119 patients were included (mean age 66 years). In multivariable analysis, age >80 years (OR 6.8, 95% CI 1.2-39.8), NIHSS at presentation >15 (OR 7.3, 95% CI 2.3-23.5), and incomplete reperfusion status (eTICI score <2C; OR 10.3, 95% CI 3.5-30.6) were the strongest predictors of a poor outcome (mRS 3-6). Adding reperfusion status to the model improved the prognostic accuracy (AUC 0.88, 95% CI 0.91-0.94). Our results indicate a large difference between using an eTICI cutoff of ≥2C versus ≥2B: a cutoff ≥2C improved the predictive value for a good clinical outcome (2C: positive predictive value, PPV, 0.78; 2B: PPV 0.32). CONCLUSION: Our results promote using reperfusion status for assessing prognosis in ischemic stroke patients treated with IAT. A model using eTICI ≥2C had greater PPV than eTICI ≥2B and could improve prognostic accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it