Aspirin Resistance in Vascular Disease: A Review Highlighting the Critical Need for Improved Point-of-Care Testing and Personalized Therapy
Bibliographic record
Abstract
Aspirin resistance describes a phenomenon where patients receiving aspirin therapy do not respond favorably to treatment, and is categorized by continued incidence of adverse cardiovascular events and/or the lack of reduced platelet reactivity. Studies demonstrate that one in four patients with vascular disease are resistant to aspirin therapy, placing them at an almost four-fold increased risk of major adverse limb and adverse cardiovascular events. Despite the increased cardiovascular risk incurred by aspirin resistant patients, strategies to diagnose or overcome this resistance are yet to be clinically validated and integrated. Currently, five unique laboratory assays have shown promise for aspirin resistance testing: Light transmission aggregometry, Platelet Function Analyzer-100, Thromboelastography, Verify Now, and Platelet Works. Newer antiplatelet therapies such as Plavix and Ticagrelor have been tested as an alternative to overcome aspirin resistance (used both in combination with aspirin and alone) but have not proven to be superior to aspirin alone. A recent breakthrough discovery has demonstrated that rivaroxaban, an anticoagulant which functions by inhibiting active Factor X when taken in combination with aspirin, improves outcomes in patients with vascular disease. Current studies are determining how this new regime may benefit those who are considered aspirin resistant.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".