Recanalization of the Chronically Occluded Internal Carotid Artery: Review of the Literature
Bibliographic record
Abstract
We reviewed the literature on interventions for patients with medically refractory chronically occluded internal carotid artery (COICA) to assess the risks and/or benefits after recanalization via an endovascular technique (ET) or hybrid surgery (HS, i.e., ET plus carotid endarterectomy). A systematic search of the electronic databases was performed. Patients with COICA were classified into 4 different categories according to Hasan et al classification. Eighteen studies satisfied the inclusion criteria. Only 6 studies involved an HS procedure. We identified 389 patients with COICA who underwent ET or HS; 91% were males. The overall perioperative complication rate was 10.1% (95% confidence interval [CI]: 7.4%–13.1%). For types A and B, the successful recanalization rate was 95.4% (95% CI: 86.5%–100%), with a 13.7% (95% CI: 2.3%–27.4%) complication rate. For type C, the success rate for ET was 45.7% (95% CI: 17.8%–70.7%), with a complication rate of 46.0% (95% CI: 20.0%–71.4%) for ET and for the HS technique 87.6% (95% CI: 80.9%–94.4%), with a complication rate of 14.0% (95% CI: 7.0%–21.8%). For type D, the success rate of recanalization was 29.8% (95% CI: 7.8%–52.8%), with a 29.8% (95% CI: 6.1%–56.3%) complication rate. Successful recanalization resulted in a symmetrical perfusion between both cerebral hemispheres, resolution of penumbra, normalization of the mean transit time, and improvement in Montreal Cognitive Assessment (MoCA) score (ΔMoCA = 9.80 points; P = 0.004). Type A and B occlusions benefit from ET, especially in the presence of a large penumbra. Type C occlusions can benefit from HS. Unfortunately, we did not identify an intervention to help patients with type D occlusions. A phase 2b randomized controlled trial is needed to confirm these findings.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".