A pooled analysis of individual patient data from trials of endarterectomy for symptomatic carotid stenosis: efficacy of surgery in important subgroups
Bibliographic record
Abstract
65 Benefit from carotid endarterectomy (CEA) depends on the degree of symptomatic stenosis, but is also likely to be influenced by other clinical and angiographic characteristics. However, individual trials have been too small to allow reliable subgroup analysis. We therefore studied pooled individual patient data from the European Carotid Surgery Trial, the North American Symptomatic Carotid Endarterectomy Trial and the Veterans Administration trial #309. We determined the effect of CEA in 11 predefined subgroups: age (<65, 65–74, 75+), sex, type of presenting event (cerebral vs ocular; TIA vs stroke; lacunar vs non-lacunar), side of presenting event, months since last event (<1, 2–3, 4+), diabetes, plaque surface irregularity, near-occlusion, and contralateral carotid occlusion. There were statistically significant interactions between the risk of ipsilateral ischaemic stroke in the medical group and 8 of the 11 subgroup variables. In the surgery group, there were interactions between the operative risk of stroke and death and 6 subgroup variables. We therefore assessed heterogeneity of overall treatment effect (any ipsilateral ischaemic stroke and surgical stroke/death) in these subgroups. There was clinically and statistically significant heterogeneity within 5 subgroups: benefit from surgery increased with age, was greater in men than women; decreased with time since presenting event; was greater after stroke than TIA, and was absent in cases of near-occlusion. For example, in patients with 50–69% stenosis, the 5 yr absolute risk reduction was 10% (95% CI = 3–10, P=0.0005) in men and -3% (95% CI = -8 - 2, P=0.8) in women (overall interaction, P=0.003). There were also important differences in the effect of surgery for lacunar and non-lacunar stroke. Patients who are most likely to benefit from CEA cannot be identified using the degree of symptomatic carotid stenosis alone. Several other clinical and angiographic characteristics influence the efficacy of surgery. Optimal selection of patients will require a risk-modelling approach using multiple baseline characteristics.
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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 | 0.049 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".