Investigation of Mechanical Thrombectomy in Elderly Patients over 85 Years Old: A Multicenter Study
Bibliographic record
Abstract
In Japan, which has a super-aging society, there are increasing opportunities to perform mechanical thrombectomy for the elderly; however, there is no recorded evidence of thrombectomy for the elderly. This study examined the usefulness of thrombectomy in the elderly. We retrospectively analyzed patient data using a multicenter acute ischemic stroke registry (NGT-FAST). We examined outcomes in patients 75 years and older who underwent thrombectomies between January 1, 2021, and December 31, 2021. The patients were divided into two groups: the 75-84-year-old group and the 85+-year-old group. There was no difference in the pretreatment National Institutes of Health Stroke Scale score or Alberta Stroke Program Early Computed Tomography Score between the two groups, but the 85+-year-old group had a significantly lower rate of pre-stroke modified Rankin Scale (mRS) score of 0-2. There were no differences in time from onset to treatment or effective recanalization rate, but complications tended to be more common in the 85+-year-old group. The number of patients with a good outcome at discharge (an mRS score of 0-3) was significantly lower in the 85+-year-old group than in the 75-84-year-old group. In addition, 90.9% of patients in the 85+-year-old group with a pre-stroke mRS score of 3 deteriorated after treatment. The pre-stroke mRS score is very important in determining the indication for thrombectomy in the elderly because their preoperative condition is more likely to influence the outcome than that of younger patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".