Treatment of Carotid Stenosis in Octogenarians: Stenting or Surgery?
Bibliographic record
Abstract
reasonably consistent across all age groups for CEA [14][15][16][17] .Subgroup analysis from North American Symptomatic Carotid Endarterectomy Trial (NASCET) suggests that the benefit from surgery was greatest amongst patients aged 75 and over, with a NNT=3 for severe symptomatic stenosis 1 .Perioperative stroke and death for CEA in patients >=75 years in NASCET was 5.2%, in comparison to the 12.1% for similarly aged CAS patients in CREST.Given the consistent safety advantage of CEA over CAS in the elderly, it would be wise to consider CEA as first-line therapy for symptomatic carotid stenosis in the elderly.High risk features for CEA (significant cardiac or pulmonary disease, history of radiation, presence of contralateral stenosis, high cervical lesions, or previous CEA), would provide reason to consider CAS.Risk analysis by Rothwell 18 suggests that even with best medical management, the stroke risk remains high in the elderly, and a treatment with up to 10% risk of perioperative stroke and death still provides a prophylactic benefit.The Calgary group notes that their perioperative mortality occurred early in their experience, and was absent in the more contemporary cohort.Improvement in their perioperative stroke and death rate is likely a composite of increasing technical skills, and patient selection. Treatment of Carotid Stenosis in Octogenarians: Stenting or Surgery?
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.
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 | 0.006 |
| 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 | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".