Bibliographic record
Abstract
Since a 2017 update, there have been important advances in stroke prevention. These include new evidence about nutrition, antiplatelet therapy, anticoagulation, lipid-lowering therapy, hypertension control, pioglitazone, and carotid endarterectomy and stenting. Evidence regarding toxic metabolites produced by the intestinal microbiome from egg yolk and red meat has important dietary implications, particularly for patients with impaired renal function, including the elderly. They should avoid egg yolk and red meat and limit the intake of animal flesh. Higher doses of folic acid may be needed for patients with the T allele of MTHFR, so it may not be sufficient to give vitamin B12 (methylcobalamin) alone, even in countries with folate fortification. There is now good evidence that lipid-lowering therapy is even more beneficial in the elderly than in younger patients; we should be using lipid-lowering therapy more intensively, often/usually combining statins with ezetimibe. There is new evidence that lower systolic blood pressure targets are better for most patients, but a subgroup with stiff arteries, a wide pulse pressure, and a diastolic pressure of <60 would be more likely to be harmed than helped by aiming for a systolic target of <120 mmHg. There is a better understanding of how the pharmacological properties of direct-acting oral anticoagulants and the metabolism of antiplatelet agents should inform decisions about the use of these agents. Pioglitazone markedly reduces the risk of stroke, both in diabetics and prediabetics; it should be used more widely. It is now clear that carotid endarterectomy is safer than stenting and that the difference is strongly affected by age. Most patients, and in particular older patients, would be better served by endarterectomy than stenting.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".